We have dataset of an application, which contains daily food intake entry for each customer. There were many ideas of problems that can be created and solved depending on this dataset, but we chose (in our opinion) the most interesting one. We will divide food into clusters, and analyse each cluster to detect what kind of food is prefered by the users of this application.
As we mentioned above, our main problem is food clustering that will be solved using different algorithms like K-Means, PCA and SOMPY. But before clustering harder part of the problem is to process our data: parse its columns, get food entries, create united food dataset, analyse, clean and fill it, prepare for clustering and so on.
To not drag this out, let's get to the task at hand.
import math
import json as js
import numpy as np
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
from scipy import stats
from kneed import KneeLocator
from warnings import filterwarnings
from collections import defaultdict
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
filterwarnings('ignore')
%matplotlib inline
sb.set_style('darkgrid')
dataset = pd.read_csv('mfp-diaries.tsv', sep = '\t', names = ['user_id', 'date', 'food_entries', 'aggregate_intake'])
dataset
| user_id | date | food_entries | aggregate_intake | |
|---|---|---|---|---|
| 0 | 1 | 2014-09-14 | [{"meal": "MY food", "dishes": [{"nutritions":... | {"total": [{"name": "Calories", "value": 2924}... |
| 1 | 1 | 2014-09-15 | [{"meal": "MY food", "dishes": [{"nutritions":... | {"total": [{"name": "Calories", "value": 2430}... |
| 2 | 1 | 2014-09-16 | [{"meal": "MY food", "dishes": [{"nutritions":... | {"total": [{"name": "Calories", "value": 1862}... |
| 3 | 1 | 2014-09-17 | [{"meal": "MY food", "dishes": [{"nutritions":... | {"total": [{"name": "Calories", "value": 2251}... |
| 4 | 1 | 2014-09-18 | [{"meal": "MY food", "dishes": [{"nutritions":... | {"total": [{"name": "Calories", "value": 2001}... |
| ... | ... | ... | ... | ... |
| 587182 | 9897 | 2015-03-02 | [{"meal": "Breakfast", "dishes": [{"nutritions... | {"total": [{"name": "Calories", "value": 1979}... |
| 587183 | 9897 | 2015-03-03 | [{"meal": "Breakfast", "dishes": [{"nutritions... | {"total": [{"name": "Calories", "value": 2141}... |
| 587184 | 9897 | 2015-03-04 | [{"meal": "Breakfast", "dishes": [{"nutritions... | {"total": [{"name": "Calories", "value": 543},... |
| 587185 | 9897 | 2015-03-14 | [{"meal": "Breakfast", "dishes": [{"nutritions... | {"total": [{"name": "Calories", "value": 2024}... |
| 587186 | 9897 | 2015-03-15 | [{"meal": "Breakfast", "dishes": [{"nutritions... | {"total": [{"name": "Calories", "value": 1496}... |
587187 rows × 4 columns
To explore our dataset data types, let's take random row and observe it.
row = 1000
user_id = dataset.iloc[row, 0]
user_id
13
date = dataset.iloc[row, 1]
date
'2015-01-31'
food_entries is a list, each element of which is a food_entry
food_entry is a dict, which consists of meal, dishes and sequence
meal is a name entered by user
dishes is a list, each element of which is a dish
dish is a dict, which consists of nutritions and name
nutritions is a list, each element of which is a nutrition
nutrition is a dict, which consists of name and value
name is a name of a nutrition
value is an amount of nutrition in that dish
name is a name of a dish
sequence is a number defining order of mealfood_entries = js.loads(dataset.iloc[row, 2])
food_entries
[{'meal': '10 AM - 2 PM',
'dishes': [{'nutritions': [{'name': 'Calories', 'value': '5'},
{'name': 'Carbs', 'value': '0'},
{'name': 'Fiber', 'value': '0'},
{'name': 'Sugar', 'value': '0'},
{'name': 'Protein', 'value': '1'},
{'name': 'Fat', 'value': '0'}],
'name': 'Coffee - Brewed from grounds, 2 cup (8 fl oz)'},
{'nutritions': [{'name': 'Calories', 'value': '100'},
{'name': 'Carbs', 'value': '1'},
{'name': 'Fiber', 'value': '0'},
{'name': 'Sugar', 'value': '0'},
{'name': 'Protein', 'value': '0'},
{'name': 'Fat', 'value': '10'}],
'name': 'Kroger - Heavy Whipping Cream, 2 Tbsp (15ml)'},
{'nutritions': [{'name': 'Calories', 'value': '220'},
{'name': 'Carbs', 'value': '0'},
{'name': 'Fiber', 'value': '0'},
{'name': 'Sugar', 'value': '0'},
{'name': 'Protein', 'value': '14'},
{'name': 'Fat', 'value': '18'}],
'name': 'Cheddar Cheese, Sharp - Tillamook, 2 oz (28 grams)'},
{'nutritions': [{'name': 'Calories', 'value': '60'},
{'name': 'Carbs', 'value': '1'},
{'name': 'Fiber', 'value': '0'},
{'name': 'Sugar', 'value': '1'},
{'name': 'Protein', 'value': '5'},
{'name': 'Fat', 'value': '5'}],
'name': 'Sargento - Sharp Cheedar Cheese String, 1 piece (21g)'},
{'nutritions': [{'name': 'Calories', 'value': '532'},
{'name': 'Carbs', 'value': '0'},
{'name': 'Fiber', 'value': '0'},
{'name': 'Sugar', 'value': '0'},
{'name': 'Protein', 'value': '49'},
{'name': 'Fat', 'value': '35'}],
'name': 'Kroger - Ground Beef 80/20, 7 oz.'}],
'sequence': 2},
{'meal': '2 PM - 6 PM',
'dishes': [{'nutritions': [{'name': 'Calories', 'value': '100'},
{'name': 'Carbs', 'value': '1'},
{'name': 'Fiber', 'value': '0'},
{'name': 'Sugar', 'value': '0'},
{'name': 'Protein', 'value': '0'},
{'name': 'Fat', 'value': '10'}],
'name': 'Kroger - Heavy Whipping Cream, 2 Tbsp (15ml)'},
{'nutritions': [{'name': 'Calories', 'value': '5'},
{'name': 'Carbs', 'value': '0'},
{'name': 'Fiber', 'value': '0'},
{'name': 'Sugar', 'value': '0'},
{'name': 'Protein', 'value': '1'},
{'name': 'Fat', 'value': '0'}],
'name': 'Coffee - Brewed from grounds, 2 cup (8 fl oz)'},
{'nutritions': [{'name': 'Calories', 'value': '104'},
{'name': 'Carbs', 'value': '26'},
{'name': 'Fiber', 'value': '4'},
{'name': 'Sugar', 'value': '16'},
{'name': 'Protein', 'value': '2'},
{'name': 'Fat', 'value': '0'}],
'name': 'Grapefruit - Large Red Grapefruit, 1 grapefruit'}],
'sequence': 3},
{'meal': '6 PM - Midnight',
'dishes': [{'nutritions': [{'name': 'Calories', 'value': '369'},
{'name': 'Carbs', 'value': '11'},
{'name': 'Fiber', 'value': '8'},
{'name': 'Sugar', 'value': '1'},
{'name': 'Protein', 'value': '9'},
{'name': 'Fat', 'value': '36'}],
'name': 'Fat Bomb - Keto "No Bake Cookies", 1 serving'},
{'nutritions': [{'name': 'Calories', 'value': '95'},
{'name': 'Carbs', 'value': '4'},
{'name': 'Fiber', 'value': '2'},
{'name': 'Sugar', 'value': '1'},
{'name': 'Protein', 'value': '5'},
{'name': 'Fat', 'value': '8'}],
'name': 'Krema - Krema Natural Peanut Butter, 1 Tbsp'}],
'sequence': 4}]
aggregate_intake is a dict, which consists of total and goal
total is a list, each element of which is a nutrition
nutrition is a dict, which consists of name and value
name is a name of a nutrition
value is a total amount of nutrition for that day
goal is a list, each element of which is a nutrition
nutrition is a dict, which consists of name and value
name is a name of a nutrition
value is a goal amount of nutrition for that dayaggregate_intake = js.loads(dataset.iloc[row, 3])
aggregate_intake
{'total': [{'name': 'Calories', 'value': 1590},
{'name': 'Carbs', 'value': 44},
{'name': 'Fiber', 'value': 14},
{'name': 'Sugar', 'value': 19},
{'name': 'Protein', 'value': 86},
{'name': 'Fat', 'value': 122}],
'goal': [{'name': 'Calories', 'value': 2150},
{'name': 'Carbs', 'value': 54},
{'name': 'Fiber', 'value': 25},
{'name': 'Sugar', 'value': 25},
{'name': 'Protein', 'value': 134},
{'name': 'Fat', 'value': 155}]}
Let's define some useful functions to check how many different nutritions we have in our dataset.
def get_unique_nutritions():
unique_nutritions = defaultdict(int)
dataset['food_entries'].apply(get_nutritions_from_row, args = [unique_nutritions])
return unique_nutritions
def get_nutritions_from_row(row, unique_nutritions):
food_entries = js.loads(row)
for entry in food_entries:
dishes = entry['dishes']
for dish in dishes:
for nutrition in dish['nutritions']:
unique_nutritions[nutrition['name']] += 1
unique_nutritions = get_unique_nutritions()
print("Unique nutritions count:", len(unique_nutritions))
unique_nutritions
Unique nutritions count: 17
defaultdict(int,
{'Calories': 6502747,
'Carbs': 6332317,
'Fat': 6300938,
'Protein': 6428946,
'Sodium': 4226244,
'Sugar': 3933324,
'Fiber': 2859976,
'Potass.': 245784,
'Iron': 367846,
'Calcium': 280697,
'Sat Fat': 190156,
'Chol': 198545,
'Vit A': 9165,
'Vit C': 24166,
'Trn Fat': 16932,
'Mon Fat': 11282,
'Ply Fat': 14614})
with plt.style.context('seaborn'):
plt.figure(figsize = (20, 10))
plt.bar(unique_nutritions.keys(), unique_nutritions.values());
As we can see, we have 17 different nutritions, but not all of them are specified by users in every food entry, let's find out exactly how many are specified by user for each food.
def get_entered_nutritions_counts():
unique_nutritions_counts = defaultdict(int)
dataset['food_entries'].apply(get_counts_from_row, args = [unique_nutritions_counts])
return unique_nutritions_counts
def get_counts_from_row(row, unique_counts):
food_entries = js.loads(row)
for entry in food_entries:
for dish in entry['dishes']:
unique_counts[str(len(dish['nutritions']))] += 1
if unique_counts[str(len(dish['nutritions']))] == 1:
print(dish['name'])
for nutrition in dish['nutritions']:
print(nutrition)
print('----------------------------------------------------------')
unique_counts = get_entered_nutritions_counts()
print('Unique nutritions count:', len(unique_counts))
unique_counts
my - McDonalds Espresso Pronto® Flat White, 2 TALL
{'name': 'Calories', 'value': '412'}
{'name': 'Carbs', 'value': '29'}
{'name': 'Fat', 'value': '24'}
{'name': 'Protein', 'value': '21'}
{'name': 'Sodium', 'value': '258'}
{'name': 'Sugar', 'value': '29'}
----------------------------------------------------------
Updated Chobani Greek Yogurt - Vanilla Non-fat, 5.3 oz 150g
{'name': 'Calories', 'value': '120'}
{'name': 'Carbs', 'value': '16'}
{'name': 'Fat', 'value': '0'}
{'name': 'Protein', 'value': '13'}
----------------------------------------------------------
Sainsbury's - Organic Kiwi Fruit, 1 fruit (80g)
{'name': 'Calories', 'value': '41'}
{'name': 'Carbs', 'value': '11'}
{'name': 'Fat', 'value': '0'}
{'name': 'Protein', 'value': '1'}
{'name': 'Fiber', 'value': '2'}
----------------------------------------------------------
Taco Bell - California Am Crunchwrap, 188 g
{'name': 'Calories', 'value': '504'}
{'name': 'Protein', 'value': '18'}
{'name': 'Carbs', 'value': '42'}
----------------------------------------------------------
Apples - Raw, with skin, 1 small (2-1/2" dia) (approx 4 per lb)
{'name': 'Calories', 'value': '55'}
----------------------------------------------------------
Mcdonalds - Double Cheese Burger No Bun, 1 DBL
{'name': 'Calories', 'value': '290'}
{'name': 'Carbs', 'value': '6'}
----------------------------------------------------------
Unique nutritions count: 6
defaultdict(int,
{'6': 5763049,
'4': 255716,
'5': 460668,
'3': 5895,
'1': 13342,
'2': 4077})
From this check we can infer that count of nutritions entered defining food can be from 1 to 6.
with plt.style.context('classic'):
plt.figure(figsize = (16, 8))
plt.bar(unique_counts.keys(), unique_counts.values());
We can see that almost 90% of food is entered with 6 defining nutritions. Which is quite nice, because more information is always good for us. Also, 7% is entered with 5 defining nutritions and almost 4% - with 4 defining nutritions. Other quanities don't even amount to 0.4 percent.
Now let's define some useful functions to check how many different types of food we have in our dataset.
def get_unique_food():
unique_food = defaultdict(int)
dataset['food_entries'].apply(get_food_from_row, args = [unique_food])
return unique_food
def get_food_from_row(row, unique_food):
food_entries = js.loads(row)
for food_entry in food_entries:
dishes = food_entry['dishes']
for dish in dishes:
unique_food[dish['name']] += 1
unique_food = get_unique_food()
print("Unique food count:", len(unique_food))
Unique food count: 1461367
Now, let's sort and check first and last 15 food by count.
def print_sorted_food(count, order):
sorted_unique_food = sorted(unique_food, key = unique_food.get, reverse = order)
for i in range(count):
food = sorted_unique_food[i]
print(str(i + 1) + ('. ' if i < 9 else '.'), food, (85 - len(food)) * '-', unique_food[food])
print_sorted_food(15, True)
1. Coffee - Brewed from grounds, 2 cup (8 fl oz) ---------------------------------------- 21011 2. Coffee - Brewed from grounds, 1 cup (8 fl oz) ---------------------------------------- 19888 3. Bananas - Raw, 1 medium (7" to 7-7/8" long) ------------------------------------------ 11256 4. Spinach - Raw, 1 cup ----------------------------------------------------------------- 7251 5. Coffee - Brewed from grounds, 3 cup (8 fl oz) ---------------------------------------- 7206 6. Cucumber - With peel, raw, 0.5 cup slices -------------------------------------------- 5370 7. Eggs - Fried (whole egg), 1 large ---------------------------------------------------- 4858 8. Eggs - Fried (whole egg), 2 large ---------------------------------------------------- 4775 9. Apples - Raw, with skin, 1 medium (2-3/4" dia) (approx 3 per lb) --------------------- 4652 10. Coffee - Brewed from grounds, 4 cup (8 fl oz) ---------------------------------------- 4450 11. Bananas - Raw, 1 small (6" to 6-7/8" long) ------------------------------------------- 4021 12. Eggs - Hard-boiled (whole egg), 1 large ---------------------------------------------- 3586 13. Strawberries - Raw, 1 cup, halves ---------------------------------------------------- 3456 14. Turbana - Banana, 1 medium banana ---------------------------------------------------- 3409 15. Spinach - Raw, 2 cup ----------------------------------------------------------------- 3295
print_sorted_food(15, False)
1. Grilld - Simply Grilled (Real), 1.25 burger ------------------------------------------ 1 2. Sainsbury's - Italian - Spaghetti Carbonara, 300 g ----------------------------------- 1 3. Beans Green - Boiled, 50 g ----------------------------------------------------------- 1 4. Mao - Seafood Laksa, 1 bowl ---------------------------------------------------------- 1 5. Dickey's - Big Bbq Sandwich, 6 oz ---------------------------------------------------- 1 6. Dickey's Bbq - Mac & Cheese, 4 oz. --------------------------------------------------- 1 7. Dickey's Bbq Pit - Spicy Cheddar Sausage, 4 oz --------------------------------------- 1 8. Peroni - Nastro Azzuro - Pale Lager 5.1, 2 bottle or can ----------------------------- 1 9. Generic - Fruit Salad, 3 CUP --------------------------------------------------------- 1 10. 12wbt - Spinach and Ricotta Cannelloni, 1 serve -------------------------------------- 1 11. Trader Joe's - Linguini With Clam Sauce, 4 cup cooked -------------------------------- 1 12. Baked Lays - Baked Lays Original, 2.5 oz bag ----------------------------------------- 1 13. Smiths Crisps Original - Salted Crisps, 2 bag 45g ------------------------------------ 1 14. Morrisons - Ham Salad Sandwich, 2 pack ----------------------------------------------- 1 15. Borg's - Spinach & Feta Cheese Spanakopita, 300 gm ----------------------------------- 1
As we can notice, there are food that are entered only once. Let's check how many similar cases are there.
sorted_unique_food = sorted(unique_food, key = unique_food.get, reverse = False)
count = 0
while unique_food[sorted_unique_food[count]] == 1:
count += 1
print('Count of food entered only once:', count)
Count of food entered only once: 820629
Around 56% of unique foods are entered only once, that can be attributed to the fact that users are entering food names on their own and there may be many typos.
It will be interesting to find out actually how many of them are really entered only once. We might check it later.
Let's create dataframe specially for foods, where each food will have it's corresponding nutritions.
def string_to_int(x):
return int(x.replace(',', ''))
def transform_to_dataframe_helper(x, food_data):
food_entries = js.loads(x)
for entry in food_entries:
dishes = entry['dishes']
for dish in dishes:
row = {'Name': dish['name'], 'Calcium': 0, 'Ply Fat': 0, 'Carbs': 0, 'Chol': 0, 'Fiber': 0, 'Protein': 0, 'Calories': 0, 'Trn Fat': 0, 'Fat': 0, 'Potass.': 0, 'Iron': 0, 'Sat Fat': 0, 'Vit A': 0, 'Sodium': 0, 'Vit C': 0, 'Sugar': 0, 'Mon Fat': 0}
for nutrition in dish['nutritions']:
row[nutrition['name']] = string_to_int(nutrition['value'])
food_data.append(row)
Following code is for creating dataframe, filtering out duplicates and adding them as new count column to unique entries. This code will be commented hence we save result dataframe and load it not to create it on every run.
# food_data = []
# dataset['food_entries'].apply(transform_to_dataframe_helper, args = [food_data])
# food_df = pd.DataFrame(food_data)
Now, let's filter out duplicates and also add count of each unique entry as new column.
# food_df = food_df.value_counts().to_frame()
# food_df.rename(columns = {0: 'Count'}, inplace = True)
# food_df.reset_index(inplace = True)
# food_df.head()
Finally, save our dataframe.
# food_df.to_csv('food_df.csv')
And load it.
food_df = pd.read_csv('food_df.csv')
food_df.drop(food_df.columns[0], inplace = True, axis = 1)
food_df.head()
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | Potass. | Iron | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 13535 |
| 1 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 11792 |
| 2 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6947 |
| 3 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5335 |
| 4 | Coffee - Brewed from grounds, 3 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 4504 |
food_df.shape
(1984590, 19)
Since we have food dataframe we can check distributions of nutritions in our data.
sb.boxplot(x = 'Calories', data = food_df);
It seems there are outliers that are spoiling the plots and data. They should be taken care of. For example:
food_df[food_df['Calories'] >= 30000]
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | Potass. | Iron | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 145113 | I'm Fat - Yolo, 10000 oz | 0 | 0 | 10000 | 0 | 1000 | 10000 | 100000 | 0 | 10000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
| 734820 | Quick Added Calories, 60,000 calories | 0 | 0 | 0 | 0 | 0 | 0 | 60000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 913826 | Brachs - Candy Corn....kim, 400 container (285... | 0 | 0 | 210000 | 0 | 0 | 0 | 840000 | 0 | 0 | 0 | 0 | 0 | 0 | 420000 | 0 | 168000 | 0 | 1 |
| 942580 | Caitlyn - Smoothie 13 2banana1EnglishCucumber2... | 0 | 0 | 0 | 0 | 0 | 0 | 385024 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1226701 | Complan - multi grain, 1 cup | 0 | 0 | 108 | 0 | 0 | 8 | 37400 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1242466 | Sierra Nevada - Pale Ale, 6000 oz | 0 | 0 | 7050 | 0 | 0 | 750 | 87500 | 0 | 0 | 0 | 0 | 0 | 0 | 7000 | 0 | 0 | 0 | 1 |
| 1361957 | Potato - Steamed Potato, 57300 g | 0 | 0 | 11460 | 0 | 0 | 974 | 49278 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 478 | 0 | 1 |
| 1404125 | Quick Added Calories, 99,999 calories | 0 | 0 | 0 | 0 | 0 | 0 | 99999 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1629175 | Kerrygold - Butter, Pure Irish, 168112 g (1Tbsp) | 0 | 0 | 0 | 0 | 0 | 0 | 1200800 | 0 | 132088 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1690682 | Kirkland Signature - Greek Yogurt (Grams), 226... | 5650 | 0 | 2260 | 0 | 0 | 5424 | 31640 | 0 | 0 | 0 | 0 | 0 | 0 | 21470 | 0 | 0 | 0 | 1 |
| 1962536 | Mcdonalds - Double Quarter Pounder With Cheese... | 0 | 0 | 500 | 0 | 0 | 4300 | 55000 | 0 | 4000 | 0 | 0 | 0 | 0 | 85000 | 0 | 100 | 0 | 1 |
| 1970773 | Mcdonald's - Mcdonald Big Mac, 1000 Sandwich | 0 | 0 | 47000 | 0 | 3000 | 0 | 530000 | 0 | 0 | 0 | 0 | 0 | 0 | 960000 | 0 | 9000 | 0 | 1 |
Those are definitely abnormal entries, some of them are typos, some of them are users being unserious. Anyway, those entries should be removed because they are polluting our data.
To remove outliers we should get a better understanding of our data and it's distribution. To do this we will use Interquartile range (IQR) method. The IQR is calculated as the difference between the 75th and the 25th percentiles of the data and defines the box in a box and whisker plot. The IQR defines the middle 50% of the data, or the body of the data. The IQR can be used to identify outliers by defining limits on the sample values that are a factor k of the IQR below the 25th percentile or above the 75th percentile. The common value for the factor k is the value 1.5. A factor k of 3 or more can be used to identify values that are extreme outliers or “far outs” when described in the context of box and whisker plots.
def get_quartiles(food_df, column_names):
quartiles = dict()
for column_name in column_names:
curr_Q1 = food_df[food_df[column_name] != 0][column_name].quantile(0.25)
curr_Q3 = food_df[food_df[column_name] != 0][column_name].quantile(0.75)
curr_IQR = curr_Q3 - curr_Q1
quartiles[column_name] = (curr_Q1, curr_IQR, curr_Q3)
return quartiles
def get_indices_to_remove(food_df, column_names):
removed_indices = dict()
for column_name in column_names:
index = food_df.index
Q1 = quartiles[column_name][0]
IQR = quartiles[column_name][1]
Q3 = quartiles[column_name][2]
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
if lower_bound < 0:
lower_bound = 0
condition = np.logical_or(food_df[column_name] < lower_bound, food_df[column_name] > upper_bound)
indices = list(index[condition])
removed_indices[column_name] = indices
return removed_indices
def filter_indices_to_remove(indices_to_remove, values_to_remove, threshold):
filtered_indices = set()
for i, index in enumerate(indices_to_remove):
if values_to_remove[i] >= 0:
break
filtered_indices.add(index)
for i, index in reversed(list(enumerate(indices_to_remove))):
if values_to_remove[i] <= threshold:
break
filtered_indices.add(index)
return filtered_indices
column_names = list(unique_nutritions.keys())
#Q1 - quartiles[column_name][0]
#IQR - quartiles[column_name][1]
#Q3 - quartiles[column_name][2]
quartiles = get_quartiles(food_df, column_names)
quartiles
{'Calories': (60.0, 170.0, 230.0),
'Carbs': (5.0, 24.0, 29.0),
'Fat': (3.0, 9.0, 12.0),
'Protein': (2.0, 12.0, 14.0),
'Sodium': (50.0, 370.0, 420.0),
'Sugar': (2.0, 9.0, 11.0),
'Fiber': (1.0, 3.0, 4.0),
'Potass.': (79.0, 280.0, 359.0),
'Iron': (2.0, 8.0, 10.0),
'Calcium': (2.0, 10.0, 12.0),
'Sat Fat': (1.0, 4.0, 5.0),
'Chol': (11.0, 57.0, 68.0),
'Vit A': (2.0, 18.0, 20.0),
'Vit C': (4.0, 34.0, 38.0),
'Trn Fat': (1.0, 1.0, 2.0),
'Mon Fat': (1.0, 5.0, 6.0),
'Ply Fat': (1.0, 2.0, 3.0)}
Now that we have Q1, IQR and Q3 scores for each nutrition column, we can get closer look at what are considered to be outliers by IQR method.
indices_to_remove = get_indices_to_remove(food_df, column_names)
unique_indices_to_remove = set()
for column in indices_to_remove:
unique_indices_to_remove = unique_indices_to_remove.union(set(indices_to_remove[column]))
print('Number of unique elements to remove:', len(unique_indices_to_remove))
Number of unique elements to remove: 331056
331056 is almost 17% of our data and it is a bit too much. We will have to look at those ourselves and see what is going on. First, let's sort indices by corresponding values for each column and also make new dict containing values.
values_to_remove = dict()
for column_name in column_names:
indices_to_remove[column_name].sort(key = lambda elem: food_df.iloc[elem, food_df.columns.get_loc(column_name)])
values_to_remove[column_name] = list(map(lambda elem: food_df.iloc[elem, food_df.columns.get_loc(column_name)],
indices_to_remove[column_name]))
Now, that we have our data in the way we want, let's take calories for example and look into more details.
len(indices_to_remove['Calories'])
109002
We can see that IQR method tells us that there are 109 thousand outliers just from calories. Let's look at how they are distributed.
print(values_to_remove['Calories'])
[-750, -620, -600, -500, -500, -500, -500, -500, -500, -500, -500, -500, -500, -500, -500, -400, -400, -400, -400, -397, -375, -370, -333, -333, -300, -300, -300, -300, -300, -300, -252, -250, -250, -250, -250, -220, -200, -200, -200, -200, -200, -189, -186, -170, -165, -140, -125, -123, -100, -100, -95, -90, -86, -84, -81, -76, -74, -70, -68, -66, -66, -66, -64, -64, -62, -60, -60, -60, -60, -60, -60, -58, -56, -54, -54, -52, -50, -48, -48, -46, -46, -44, -44, -44, -43, -42, -40, -40, -40, -40, -38, -34, -28, -25, -25, -21, -20, -20, -16, -15, -5, -4, -3, -1, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 486, 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1980, 1980, 1984, 1984, 1989, 1990, 1990, 1990, 1990, 1994, 1995, 1995, 1995, 1995, 1995, 1997, 1998, 1999, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2002, 2005, 2006, 2007, 2008, 2008, 2010, 2013, 2016, 2020, 2020, 2023, 2024, 2024, 2025, 2025, 2025, 2025, 2027, 2028, 2031, 2033, 2034, 2036, 2036, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2040, 2043, 2044, 2046, 2050, 2050, 2050, 2052, 2052, 2053, 2054, 2054, 2055, 2056, 2060, 2060, 2060, 2063, 2064, 2064, 2064, 2066, 2070, 2070, 2070, 2072, 2073, 2075, 2075, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2080, 2083, 2085, 2086, 2090, 2090, 2090, 2093, 2097, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2100, 2104, 2105, 2106, 2110, 2118, 2118, 2120, 2121, 2125, 2125, 2130, 2130, 2130, 2132, 2132, 2133, 2135, 2136, 2142, 2145, 2145, 2148, 2150, 2150, 2156, 2160, 2160, 2160, 2160, 2160, 2160, 2160, 2160, 2160, 2160, 2160, 2160, 2160, 2168, 2170, 2170, 2170, 2170, 2175, 2177, 2187, 2188, 2191, 2192, 2196, 2198, 2200, 2200, 2200, 2200, 2200, 2200, 2200, 2207, 2210, 2216, 2216, 2220, 2220, 2220, 2222, 2230, 2235, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2240, 2248, 2248, 2250, 2250, 2250, 2250, 2250, 2250, 2250, 2250, 2257, 2267, 2268, 2270, 2270, 2272, 2273, 2280, 2280, 2280, 2280, 2280, 2290, 2291, 2294, 2300, 2300, 2300, 2300, 2307, 2310, 2310, 2310, 2313, 2320, 2320, 2320, 2320, 2320, 2320, 2320, 2327, 2328, 2333, 2340, 2340, 2340, 2340, 2340, 2347, 2350, 2351, 2360, 2362, 2362, 2366, 2370, 2370, 2370, 2375, 2380, 2380, 2381, 2383, 2388, 2391, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2400, 2408, 2415, 2415, 2420, 2424, 2425, 2430, 2439, 2440, 2440, 2442, 2450, 2450, 2450, 2450, 2454, 2454, 2459, 2467, 2470, 2470, 2470, 2475, 2480, 2480, 2480, 2480, 2480, 2480, 2480, 2480, 2480, 2484, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2506, 2511, 2520, 2520, 2520, 2520, 2520, 2520, 2520, 2520, 2520, 2530, 2530, 2534, 2544, 2550, 2550, 2550, 2554, 2556, 2560, 2560, 2560, 2565, 2580, 2584, 2586, 2592, 2594, 2600, 2600, 2600, 2600, 2600, 2600, 2610, 2610, 2614, 2625, 2640, 2640, 2640, 2640, 2640, 2640, 2660, 2676, 2680, 2688, 2690, 2698, 2700, 2700, 2700, 2700, 2710, 2720, 2720, 2720, 2720, 2720, 2720, 2720, 2744, 2750, 2753, 2760, 2780, 2787, 2800, 2800, 2800, 2800, 2800, 2800, 2800, 2800, 2800, 2823, 2828, 2828, 2831, 2832, 2832, 2838, 2853, 2861, 2864, 2880, 2880, 2880, 2880, 2880, 2880, 2880, 2889, 2892, 2900, 2900, 2913, 2940, 2950, 2960, 2960, 2975, 2975, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3008, 3016, 3040, 3055, 3100, 3100, 3120, 3120, 3121, 3125, 3139, 3150, 3150, 3150, 3190, 3200, 3200, 3200, 3200, 3200, 3202, 3230, 3250, 3260, 3274, 3280, 3280, 3284, 3300, 3300, 3320, 3321, 3330, 3332, 3336, 3348, 3400, 3400, 3400, 3402, 3410, 3480, 3483, 3491, 3500, 3500, 3500, 3500, 3500, 3500, 3500, 3500, 3500, 3500, 3520, 3555, 3570, 3599, 3600, 3603, 3616, 3636, 3663, 3672, 3690, 3700, 3750, 3753, 3774, 3819, 3825, 3840, 3840, 3854, 3900, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4008, 4050, 4073, 4080, 4140, 4200, 4200, 4250, 4260, 4310, 4463, 4480, 4480, 4500, 4580, 4650, 4700, 4731, 4830, 4883, 4900, 4928, 5000, 5000, 5000, 5250, 5260, 5340, 5400, 5440, 5500, 5760, 5800, 6000, 6000, 6090, 6300, 6426, 6584, 6600, 6825, 6930, 6960, 6970, 7000, 7680, 7875, 8000, 8264, 8500, 9000, 9000, 9200, 9450, 9600, 9880, 9900, 9900, 10000, 10000, 10000, 10185, 10290, 10395, 10500, 10500, 10710, 11000, 11500, 12000, 12364, 12600, 13125, 14280, 15000, 19250, 19740, 20000, 21000, 21000, 21750, 24600, 29000, 31640, 37400, 49278, 55000, 60000, 87500, 99999, 100000, 385024, 530000, 840000, 1200800]
First thing that catches our eye is that there are foods with negative calories. Let's observe them more closely.
print(food_df.iloc[indices_to_remove['Calories'][0]])
print("---------------------------------------------------------")
print(food_df.iloc[indices_to_remove['Calories'][40]])
Name Generic - Breastfeeding Exclusively, 40.5 oz. Calcium 0 Ply Fat 0 Carbs -75 Chol 0 Fiber 0 Protein -47 Calories -750 Trn Fat 0 Fat -24 Potass. 0 Iron 0 Sat Fat 0 Vit A 0 Sodium 0 Vit C 0 Sugar 0 Mon Fat 0 Count 1 Name: 838081, dtype: object --------------------------------------------------------- Name Quick Added Calories, -200 calories Calcium 0 Ply Fat 0 Carbs 0 Chol 0 Fiber 0 Protein 0 Calories -200 Trn Fat 0 Fat 0 Potass. 0 Iron 0 Sat Fat 0 Vit A 0 Sodium 0 Vit C 0 Sugar 0 Mon Fat 0 Count 2 Name: 734689, dtype: object
We can see that these are not foods, so it is perfectly fine to remove these types of entries from our food database.
After this we can notice that IQR method says that food with 486 calories is an outlier. But it is very normal for food to have 486 calories, for example, 1 large McDonald's French Fries is 500 calories. So, as suspected, IQR method doesn't filter out outliers very well and we have to interfere manually. At this point it would be good to employ help of expert in this sphere. But for now let's do this ourselves.
After some research, of which we shall not go into details (it involved lot of googling food compositions in general and also comparing it to our data), it was decided that maximal calories for one dish should be 1500 and everything above that is an outlier and should be removed.
updated_indices_to_remove = dict()
updated_indices_to_remove['Calories'] = filter_indices_to_remove(indices_to_remove['Calories'], values_to_remove['Calories'], 1500)
print('Calories', '------------------------------------------', 'Deleted:', len(updated_indices_to_remove['Calories']), '/', len(indices_to_remove['Calories']))
Calories ------------------------------------------ Deleted: 2018 / 109002
Now that we have indices of elements that we acknoweldge as outliers of calories, we can move on to next nutritions.
We won't go into every step of outlier removal process for every nutrition. They are same as for calories. Thresholds list contains decided individual thresholds for respective nutritions.
nutritions_without_calories = ['Carbs', 'Fat', 'Protein', 'Sodium', 'Sugar', 'Fiber', 'Potass.', 'Iron', 'Calcium', 'Sat Fat', 'Chol', 'Vit A', 'Vit C', 'Trn Fat', 'Mon Fat', 'Ply Fat']
thresholds = [250, 120, 95, 4600, 150, 39, 1800, 100, 106, 35, 650, 500, 300, 16, 20, 14]
for i in range(len(nutritions_without_calories)):
nutrition_name = nutritions_without_calories[i]
nutrition_threshold = thresholds[i]
updated_indices_to_remove[nutrition_name] = filter_indices_to_remove(indices_to_remove[nutrition_name], values_to_remove[nutrition_name], nutrition_threshold)
print(nutrition_name, (50 - len(nutrition_name)) * '-', 'Deleted:', len(updated_indices_to_remove[nutrition_name]), '/', len(indices_to_remove[nutrition_name]))
Carbs --------------------------------------------- Deleted: 802 / 74023 Fat ----------------------------------------------- Deleted: 959 / 95512 Protein ------------------------------------------- Deleted: 2589 / 111226 Sodium -------------------------------------------- Deleted: 702 / 78178 Sugar --------------------------------------------- Deleted: 446 / 58462 Fiber --------------------------------------------- Deleted: 400 / 28127 Potass. ------------------------------------------- Deleted: 146 / 2098 Iron ---------------------------------------------- Deleted: 207 / 5615 Calcium ------------------------------------------- Deleted: 219 / 5050 Sat Fat ------------------------------------------- Deleted: 79 / 2251 Chol ---------------------------------------------- Deleted: 114 / 2378 Vit A --------------------------------------------- Deleted: 16 / 247 Vit C --------------------------------------------- Deleted: 50 / 385 Trn Fat ------------------------------------------- Deleted: 3 / 26 Mon Fat ------------------------------------------- Deleted: 2 / 13 Ply Fat ------------------------------------------- Deleted: 6 / 59
updated_unique_indices_to_remove = set()
for indices in updated_indices_to_remove.values():
updated_unique_indices_to_remove = updated_unique_indices_to_remove.union(indices)
updated_unique_indices_to_remove = list(updated_unique_indices_to_remove)
print('Number of outliers:', len(updated_unique_indices_to_remove))
Number of outliers: 6958
It looks like from our observations that 0.35% of our data were outliers, let's delete them from dataframe.
deleted_data = food_df.loc[updated_unique_indices_to_remove, : ]
food_df.drop(updated_unique_indices_to_remove, inplace = True)
food_df.reset_index(inplace = True, drop = True)
Let us double check and double filter our results with another method of detecting outliers. We shall check it with projection method.
Projection methods are relatively simple to apply and quickly highlight extraneous values.
We shall use PCA for reducing dimensionality. Also it will help us identify outliers with multivariate method.
For PCA to function well, it is neccessary that data is standartized. It is even more important for our data because aside from having different variances, not everything is measured in same measurements, e.g. grams.
nutrition_values = food_df.iloc[:, 1:18].values
nutrition_values = StandardScaler().fit_transform(nutrition_values)
pca = PCA(n_components = 2, svd_solver = 'full')
data_2D = pca.fit_transform(nutrition_values)
pca_df = pd.DataFrame(data = data_2D, columns = ['Principal Component 1', 'Principal Component 2'])
pca_df.head()
| Principal Component 1 | Principal Component 2 | |
|---|---|---|
| 0 | -1.705750 | 0.198707 |
| 1 | -1.752636 | 0.165696 |
| 2 | -1.757540 | 0.163599 |
| 3 | -1.714576 | 0.194931 |
| 4 | -1.694233 | 0.200698 |
sum(pca.explained_variance_ratio_)
0.24688894506824655
Having only 25% of variance retained is probably bad, but we don't know exactly how bad it is. So, we have 2 options:
Let's go through with second option.
plt.figure(figsize = (18, 12))
plt.scatter(data_2D[:, 0], data_2D[:, 1]);
# For 3D plot
# fig = plt.figure(figsize = (25, 25))
# ax = plt.axes(projection = '3d')
# scatter = ax.scatter(data_2D[:, 0], data_2D[:, 1], data_2D[:, 2])
# plt.show()
Now let's draw some red lines for what we think are outliers. Everything that is outside of any red line will be considered as outlier.
# Points of lines that will set borders for outliers
x1_1, y1_1, x2_1, y2_1 = -2.5, 20, 16.2, 8
x1_2, y1_2, x2_2, y2_2 = -2.5, -22.7, 16.2, -11
x1_3, y1_3, x2_3, y2_3 = 13, -20, 16.2, 0
x1_4, y1_4, x2_4, y2_4 = 12, 20, 16.6, 0
x1_5, y1_5, x2_5, y2_5 = -2.5, 2, 5, 25
plt.figure(figsize = (18, 12))
plt.axline((x1_1, y1_1), (x2_1, y2_1), linewidth = 1, color = 'r')
plt.axline((x1_2, y1_2), (x2_2, y2_2), linewidth = 1, color = 'r')
plt.axline((x1_3, y1_3), (x2_3, y2_3), linewidth = 1, color = 'r')
plt.axline((x1_4, y1_4), (x2_4, y2_4), linewidth = 1, color = 'r')
plt.axline((x1_5, y1_5), (x2_5, y2_5), linewidth = 1, color = 'r')
plt.scatter(data_2D[:, 0], data_2D[:, 1]);
# This function will return k and b coefficients of the linear function that contains given coordinates
def get_k_and_b(x1, y1, x2, y2):
k = (y2 - y1) / (x2 - x1)
b = y1 - k * x1
return k, b
filtered_indices = []
# Get k and b for each line
k1, b1 = get_k_and_b(x1_1, y1_1, x2_1, y2_1)
k2, b2 = get_k_and_b(x1_2, y1_2, x2_2, y2_2)
k3, b3 = get_k_and_b(x1_3, y1_3, x2_3, y2_3)
k4, b4 = get_k_and_b(x1_4, y1_4, x2_4, y2_4)
k5, b5 = get_k_and_b(x1_5, y1_5, x2_5, y2_5)
Now, let's filter all points and remember outliers.
for row in pca_df.itertuples():
x = row._1
y = row._2
y1 = k1 * x + b1
y2 = k2 * x + b2
y3 = k3 * x + b3
y4 = k4 * x + b4
y5 = k5 * x + b5
if (y >= y1) or (y <= y2) or (y <= y3) or (y >= y4) or (y >= y5):
filtered_indices.append(row.Index)
print('Count of outliers found with PCA:', len(filtered_indices))
Count of outliers found with PCA: 99
deleted_data_with_pca = food_df.loc[filtered_indices, :]
food_df.drop(filtered_indices, inplace = True)
food_df.reset_index(inplace = True, drop = True)
At the end, we can take a look at removed outliers.
deleted_data = pd.concat([deleted_data, deleted_data_with_pca])
deleted_data
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | Potass. | Iron | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1294336 | Sugars - Brown, 233.33 g | 0 | 0 | 227 | 0 | 0 | 0 | 880 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 224 | 0 | 1 |
| 1851393 | Papa Johns - Pizza - Medium Peperoni and Sausa... | 0 | 0 | 208 | 0 | 0 | 72 | 1840 | 0 | 72 | 0 | 0 | 0 | 0 | 4640 | 0 | 24 | 0 | 1 |
| 1392645 | Sa - Spare Rib Pizza, 1 container (8 slices ea.) | 0 | 0 | 14 | 0 | 0 | 138 | 2831 | 0 | 154 | 0 | 0 | 0 | 0 | 3796 | 0 | 0 | 0 | 1 |
| 1851397 | Papa Johns - Pizza - Medium Peperoni and Sausa... | 0 | 0 | 182 | 0 | 0 | 63 | 1610 | 0 | 63 | 0 | 0 | 0 | 0 | 4060 | 0 | 21 | 0 | 1 |
| 671751 | Thr1ve - Chicken Bowl - Main Size (Jan 2015), ... | 0 | 0 | 7 | 0 | 0 | 44 | 503 | 0 | 33 | 0 | 0 | 0 | 0 | 4683 | 0 | 3 | 0 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1954372 | Mcdonalds - Big Breakfast, 9.5 oz | 0 | 0 | 51 | 555 | 0 | 28 | 740 | 0 | 48 | 0 | 0 | 0 | 0 | 1560 | 0 | 0 | 0 | 1 |
| 1955766 | Mcdonald's - Sausage Egg Mcmuffin (Canada), 2 ... | 0 | 0 | 64 | 440 | 0 | 40 | 880 | 0 | 52 | 0 | 0 | 20 | 0 | 0 | 0 | 0 | 0 | 1 |
| 1955969 | Mcdonald's - Sausage Mcmuffin With Egg, 2 sand... | 0 | 0 | 60 | 570 | 4 | 0 | 900 | 0 | 54 | 0 | 0 | 0 | 0 | 1840 | 0 | 0 | 0 | 1 |
| 1969579 | Migas 121614, 1 serving(s) | 0 | 0 | 29 | 622 | 0 | 37 | 601 | 0 | 36 | 0 | 0 | 0 | 0 | 1070 | 0 | 0 | 0 | 1 |
| 1974293 | Meathead Ice Cream #2, 2 serving(s) | 0 | 0 | 34 | 644 | 0 | 20 | 649 | 0 | 49 | 0 | 0 | 0 | 0 | 290 | 0 | 0 | 0 | 1 |
7057 rows × 19 columns
food_df.head()
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | Potass. | Iron | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 13535 |
| 1 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 11792 |
| 2 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6947 |
| 3 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5335 |
| 4 | Coffee - Brewed from grounds, 3 cup (8 fl oz) | 0 | 0 | 0 | 0 | 0 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 4504 |
As we can see in our dataset there are same foods with different amounts of nutritions. This can be caused by different reasons:
calories/sugar and doesn't care about other nutritions that food consists of);Because of this reasons majority of food appears several times in our dataset. To treat all this issues we can simply merge all different entries of same food into one, with logic that follows:
Our goal is to not have zero values in any column, for this we shall replace them if possible.
def column_function(column, count, output_row):
total_count = sum(count)
if column.name == 'Count':
output_row.append(total_count)
return
value_counts = defaultdict(int)
for index, value in column.iteritems():
value_counts[value] += count[index]
values = sorted(value_counts, key = value_counts.get, reverse = True)
#If we have only 1 value, we dont change anything
if len(values) > 1:
non_zero_count = total_count - value_counts[0]
mode = values[0]
# We need non-zero mode
if mode == 0:
mode = values[1]
replace_value = mode # If mode is more than 0.2, values must be replaced with mode
if value_counts[mode] / non_zero_count < 0.2 or value_counts[mode] < 3:
replace_value = 0 # If zero percentage is more than 0.8, values must be replaced with zero
if value_counts[0] / total_count < 0.8:
# In this case values must be replaced with average
replace_value = sum(column) / non_zero_count
output_row.append(replace_value)
else:
output_row.append(values[0])
def group_function(group_df):
row = []
group_df.apply(column_function, axis = 0, count = group_df['Count'], output_row = row)
return pd.DataFrame(data = [row], columns = food_df.columns)
To be honest, writing and running above functions took us several days and we aren't going to run them again, ever. So, we strictly decided to save the output as CSV and load it on every run. In addition, running this function is not advisable, since it takes up to 2 hours of execution time, but previous 15 versions took much more (:D).
# unique_food_df = food_df.groupby('Name', sort = False).apply(group_function)
# unique_food_df.to_csv('unique_food_df.csv')
food_df = pd.read_csv('unique_food_df.csv')
food_df.drop([food_df.columns[1], food_df.columns[2]], inplace = True, axis = 1)
food_df.head()
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | Potass. | Iron | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 5.0 | 0.0 | 0.0 | 232.0 | 0.0 | 0.0 | 0.0 | 9.0 | 0.0 | 0.0 | 0.0 | 21011 |
| 1 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 116.0 | 0.0 | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 0.0 | 19888 |
| 2 | Coffee - Brewed from grounds, 3 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 7.0 | 0.0 | 0.0 | 348.0 | 0.0 | 0.0 | 0.0 | 14.0 | 0.0 | 0.0 | 0.0 | 7206 |
| 3 | Spinach - Raw, 1 cup | 3.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 7.0 | 0.0 | 0.0 | 167.0 | 5.0 | 0.0 | 56.0 | 24.0 | 14.0 | 0.0 | 0.0 | 7251 |
| 4 | Eggs - Fried (whole egg), 1 large | 3.0 | 0.0 | 0.0 | 210.0 | 0.0 | 6.0 | 92.0 | 0.0 | 7.0 | 68.0 | 5.0 | 2.0 | 7.0 | 94.0 | 0.0 | 0.0 | 0.0 | 4858 |
Finally, our data looks like it's pretty clean. We reached our goal of having one unique entry for each food, that consists of collated data of multiple entries. We can look and check that it worked fine, since we have three different portions of Coffee - Brewed from grounds, each of them are unique and they are correlated with each others sizes (2 cup portion's nutritions are twice greater than 1 cup portion's nutritions, and 3 cup portion's nutritions are three times greater than 1 cup portion's nutritions). And same goes for almost every other entry.
Now that we have our data how we want it, it will be pretty interesting to explore more details and truly see what we have to work with. Let's start with univariate analysis.
column_name = 'Calcium'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
This plot looks pretty bad, let's check why is that.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Calciumis non-zero in:39057/1455111cases (2.6841251285984367%)
As we can see Calcium has non-zero value only in 2.7% of our data and that explains our plot's look. Let's plot only non-zero values.
with plt.style.context('ggplot'):
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
We can see that in high majority of cases when Calcium is not 0 it is less that 10, while range is from 0 to 106.
column_name = 'Ply Fat'
sb.distplot(food_df[column_name]);
It looks even worse than case of Calcium, let's look at it more closely.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Ply Fatis non-zero in:416/1455111cases (0.02858888428442916%)
We can see that Ply Fat has non-zero value only in 0.02% of our data, which is terribly low. Let's plot only non-zero values.
food_df[food_df[column_name] > 0][column_name].hist(figsize=(10, 5));
We can see that most common value for Ply Fat when it is not 0, is 1 and it's range varies from 1 to 13.
column_name = 'Carbs'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
Well, this plot looks different from above cases, let's get into more details.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Carbsis non-zero in:1179868/1455111cases (81.08439837235785%)
As we can see we have value of Carbs for 4/5 of our food. Let's see more of the plots.
with plt.style.context('ggplot'):
sb.boxplot(x = column_name, data = food_df);
with plt.style.context('ggplot'):
food_df[column_name].hist(figsize = (10, 5));
food_df[food_df[column_name] <= 25].shape[0] / food_df.shape[0]
0.7515282339285456
We can see that 75% of food have Carbs value of less than 25.
column_name = 'Chol'
sb.distplot(food_df[column_name]);
It looks like similar case of Calcium
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Cholis non-zero in:20506/1455111cases (1.409239570039674%)
As we can see Chol has non-zero value only in 1.4% of our data and that explains our plot's look. Let's plot only non-zero values.
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
sb.boxplot(x = column_name, data = food_df[food_df[column_name] > 0]);
We can see that in high majority of cases when Chol is not 0 it is less that 50 and it varies from 0 to 648.
column_name = 'Fiber'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
This plot is different from every above plot, let's investigate more.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Fiberis non-zero in:370748/1455111cases (25.479018439143132%)
Above plot can be explained by the fact that Fiber has non-zero in 1/4 of our data, so it still contains 75% zeroes which is still high number, so let's plot non-zero entries.
with plt.style.context('ggplot'):
sb.distplot(food_df[food_df[column_name] > 0][column_name]);
We can see that in high majority of cases when Fiber is not 0 it has value from 0 to 5, while it'srange varies from 0 to 39.
column_name = 'Protein'
sb.distplot(food_df[column_name]);
It looks like we have similar case of Carbs.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Proteinis non-zero in:1157045/1455111cases (79.51592696364744%)
Our supposition above was correct, Protein is non-zero entered in almost 4/5 cases just like Carbs. Let's see more of the plots.
sb.boxplot(x = column_name, data = food_df);
food_df[column_name].hist(figsize = (10, 5));
food_df[food_df[column_name] < 12].shape[0] / food_df.shape[0]
0.7500609919105827
Plots also look like Carbs plots but numbers ar changed. So if 75% of food had Carbs value of less than 25, in this case 75% of food have Protein value of less than 12.
column_name = 'Calories'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
We aleardy knew that Calories would have best plot, because it is most entered nutrition for all foods. Let's check percentage of entries.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Caloriesis non-zero in:1431129/1455111cases (98.35187831031446%)
As suspected almost every food has Calories value, only 1.65% doesn't. Let's see the distributions.
with plt.style.context('ggplot'):
food_df[column_name].hist(figsize = (10, 5));
with plt.style.context('ggplot'):
sb.boxplot(x = column_name, data = food_df);
print('Food percentage under 300 Calories:', food_df[food_df[column_name] <= 300].shape[0] / food_df.shape[0] * 100, '%')
Food percentage under 300 Calories: 83.28697948129043 %
print('Mean:', food_df[column_name].mean())
Mean: 175.90405266963097
We can see that high majority of entered food(83%) has Calories value of less than 300, also mean for foods is 176 Calories.
column_name = 'Trn Fat'
sb.distplot(food_df[column_name]);
This plot looks pretty bad, let's check why is that.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Trn Fatis non-zero in:143/1455111cases (0.009827428972772523%)
As we can see Trn Fat has non-zero value only in 0.01% of our data and that explains our plot's look. Let's plot only non-zero values.
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
print('Percentage of Trn Fat with value 1:',
food_df[food_df[column_name] == 1].shape[0] / food_df[food_df[column_name] > 0].shape[0] * 100,
'%')
Percentage of Trn Fat with value 1: 60.83916083916085 %
We can see that in majority of cases (61%) when Trn Fat is not 0 it is equal to 1.
column_name = 'Fat'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
Let's check non-zero count of Fat.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Fatis non-zero in:1025351/1455111cases (70.46548338923972%)
As we can see Fat has non-zero value in 70% of our data and that explains our plot's look. Let's look more into the distributions.
with plt.style.context('ggplot'):
food_df[column_name].hist(figsize = (10, 5));
print('Food percentage under 12 Fat:', food_df[food_df[column_name] < 12].shape[0] / food_df.shape[0] * 100, '%')
Food percentage under 12 Fat: 80.1791753343903 %
print('Mean:', food_df[column_name].mean())
Mean: 6.724742971989776
We can see that 4/5 of enterd food has Fat value of less than 12, also mean for foods is 6.7 Fat.
column_name = 'Potass.'
sb.distplot(food_df[column_name]);
This plot looks looks like case of Calcium, let's check it out.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Potass.is non-zero in:22837/1455111cases (1.569433534623819%)
As we can see Potas. has non-zero value only in 1.5% of our data and that explains our plot's look. Let's plot only non-zero values.
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
sb.distplot(food_df[food_df[column_name] > 0][column_name]);
print('Mean:', food_df[food_df[column_name] > 0][column_name].mean())
Mean: 269.3119090365051
We can see that most of the food have Potass. value less than 500 while it is non-zero with mean of 269.
column_name = 'Iron'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
Another one with look of case Calcium
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Ironis non-zero in:52081/1455111cases (3.5791771211955656%)
As we can see Iron has non-zero value only in 3.5% of our data and that explains our plot's look. Let's plot only non-zero values.
with plt.style.context('ggplot'):
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
We can see that in high majority of cases when Iron is not 0 it is less that 10.
column_name = 'Sat Fat'
sb.distplot(food_df[column_name]);
Yet another case of case Calcium.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Sat Fatis non-zero in:28213/1455111cases (1.9388898853764422%)
As we can see Sat Fat has non-zero value only in 1.9% of our data and that explains our plot's look. Let's plot only non-zero values.
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
We can see that in high majority of cases when Sat Fat is not 0 it is less that 7.
column_name = 'Vit A'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
This plot looks like case of Ply Fat, let's check the similiarities.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Vit Ais non-zero in:1039/1455111cases (0.07140348743154303%)
As we can see Vit A has non-zero value only in 0.07% of our data and that explains our plot's look also similarity to case Ply Fat which had 0.02% of non-zero data. Let's plot only non-zero values.
with plt.style.context('ggplot'):
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
We can see that in high majority almost 90% of cases when Vit A is not 0 it is in 0-50 range.
column_name = 'Sodium'
sb.distplot(food_df[column_name]);
Let's check out non-zero percentage of Sodium.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Sodiumis non-zero in:798209/1455111cases (54.855540230264225%)
As we can see Sodium has non-zero value only in 55% of our data, which is not bad. Let's plot both plots: only non-zero and whole.
food_df[column_name].hist(figsize = (10, 5));
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
sb.distplot(food_df[food_df[column_name] > 0][column_name]);
Now let's compare means.
print('Only non-zero mean:', food_df[food_df[column_name] > 0][column_name].mean(), '\nActual mean:', food_df[column_name].mean())
Only non-zero mean: 329.4004611002083 Actual mean: 180.6944024575007
It is clear that most of the Sodium is under 500 mark with mean of 329, which drops to 180 when we also count zeroes in.
column_name = 'Vit C'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
This plot looks almost same as Ply Fat case.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Vit Cis non-zero in:1832/1455111cases (0.12590104809873612%)
As we can see Calcium has non-zero value only in 0.12% of our data and that explains our plot's look. Let's plot only non-zero values.
with plt.style.context('ggplot'):
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
We can see that in high majority of cases when Vit C is not 0 it is less that 25.
column_name = 'Sugar'
sb.distplot(food_df[column_name]);
This plot looks like a case of Sodium.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Sugaris non-zero in:591434/1455111cases (40.64528410547374%)
As we can see Sugar has non-zero value only in 41% of our data and that explains our plot's look. Let's plot both plots: only non-zero and whole.
food_df[column_name].hist(figsize = (10, 5));
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
sb.distplot(food_df[food_df[column_name] > 0][column_name]);
Now let's compare means.
print('Only non-zero mean:', food_df[food_df[column_name] > 0][column_name].mean(), '\nActual mean:', food_df[column_name].mean())
Only non-zero mean: 9.194218589794389 Actual mean: 3.737016267100209
It is clear that most of the Sugar is under 15 mark with mean of 9, which drops to 3.7 when we also count zeroes in.
column_name = 'Mon Fat'
with plt.style.context('ggplot'):
sb.distplot(food_df[column_name]);
This looks like yet another case of Ply Fat, let's check it out.
print(column_name, 'is non-zero in:', food_df[food_df[column_name] > 0].shape[0], '/', food_df.shape[0], 'cases (',
food_df[food_df[column_name] > 0].shape[0] / food_df.shape[0] * 100, '%)', sep = '')
Mon Fatis non-zero in:209/1455111cases (0.01436316542174446%)
As we can see Calcium has non-zero value only in 0.014% of our data and that explains our plot's look. Let's plot only non-zero values.
with plt.style.context('ggplot'):
food_df[food_df[column_name] > 0][column_name].hist(figsize = (10, 5));
with plt.style.context('ggplot'):
sb.boxplot(x = column_name, data = food_df[food_df[column_name] > 0]);
We can see that in high majority of cases when Mon Fat is not 0 it is less or equal than 5. Also it's mean is almost 2.5.
After all this now let's build correlations matrix to see correlations between nutritions.
We will use the heatmap to visualize the correlations between the variables.
matrix = food_df.corr()
f, ax = plt.subplots(figsize = (15, 8))
with sb.axes_style('white'):
sb.heatmap(matrix, mask = np.triu(matrix, 1), annot = True, fmt = '0.2f', vmax = 0.8, cbar = False, cmap = 'coolwarm');
Looking at the heatmap we can notice that Calories is in correlation with Fat,Carbs,Protein, Sodium. Also, Sugar and Carbs are correlated with each other too. These correlations are probably incurred by the fact that Calories and nutrients that are correlated to it are probably most "popular" ones, so that in majority of cases these nutritions are entered and others are mostly zeroes. But high correlations like Calories and Fat or Calories and Carbs, will not be explained only by that, they are probably truly correlated.
As we have seen above, some nutritions are entered very rarely. We can again look at nutritions sorted percenteges.
nutrition_percentages = dict()
for column in column_names:
nutrition_percentages[column] = food_df[food_df[column] > 0].shape[0] / food_df.shape[0] * 100
sorted_nutritions = sorted(nutrition_percentages, key = nutrition_percentages.get, reverse = True)
for nutrition in sorted_nutritions:
print(nutrition, (20 - len(nutrition)) * '-', str(round(nutrition_percentages[nutrition], 2)),
' %' if nutrition_percentages[nutrition] < 10 else '%')
Calories ------------ 98.35 % Carbs --------------- 81.08 % Protein ------------- 79.52 % Fat ----------------- 70.47 % Sodium -------------- 54.86 % Sugar --------------- 40.65 % Fiber --------------- 25.48 % Iron ---------------- 3.58 % Calcium ------------- 2.68 % Sat Fat ------------- 1.94 % Potass. ------------- 1.57 % Chol ---------------- 1.41 % Vit C --------------- 0.13 % Vit A --------------- 0.07 % Ply Fat ------------- 0.03 % Mon Fat ------------- 0.01 % Trn Fat ------------- 0.01 %
As we can see, there are 10 nutritions that are present in less then 4% of food entries, others are more than 25%. Since we are going to cluster food, these nutritions will pollute clusters, hence this information is very poor. So, we should not include them in clustering variables, that will also reduce dimensionality of clustering data.
clustering_variables = ['Calories', 'Carbs', 'Protein', 'Fat', 'Sodium', 'Sugar', 'Fiber']
Another description that can be applied to food is how heavy (მსუყე/მძიმე) it is. That can be determined by three nutritions: Calories, Fat, Sugar. So we can take their sum as measure of heaviness. But there is one problem their ranges and variances differ highly. That is a problem because for example: food with (0 Calories, 0 Fat, and 20 Sugar) will be way more heavy than food with (100 Calories, 0 Fat and 0 Sugar). To solve that problem, we have to normalize each column and then take their sum. So range of Food Heaviness will be [0,3].
scaler = MinMaxScaler()
food_df['Food Heaviness'] = scaler.fit_transform(food_df[['Calories','Fat','Sugar']]).sum(axis = 1)
food_df.head()
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | Potass. | Iron | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | Food Heaviness | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 5.0 | 0.0 | 0.0 | 232.0 | 0.0 | 0.0 | 0.0 | 9.0 | 0.0 | 0.0 | 0.0 | 21011 | 0.003333 |
| 1 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 116.0 | 0.0 | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 0.0 | 19888 | 0.001333 |
| 2 | Coffee - Brewed from grounds, 3 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 7.0 | 0.0 | 0.0 | 348.0 | 0.0 | 0.0 | 0.0 | 14.0 | 0.0 | 0.0 | 0.0 | 7206 | 0.004667 |
| 3 | Spinach - Raw, 1 cup | 3.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 7.0 | 0.0 | 0.0 | 167.0 | 5.0 | 0.0 | 56.0 | 24.0 | 14.0 | 0.0 | 0.0 | 7251 | 0.004667 |
| 4 | Eggs - Fried (whole egg), 1 large | 3.0 | 0.0 | 0.0 | 210.0 | 0.0 | 6.0 | 92.0 | 0.0 | 7.0 | 68.0 | 5.0 | 2.0 | 7.0 | 94.0 | 0.0 | 0.0 | 0.0 | 4858 | 0.119667 |
Now let's analyse our new feature.
with plt.style.context('grayscale'):
sb.distplot(food_df['Food Heaviness']);
with plt.style.context('grayscale'):
food_df['Food Heaviness'].hist(figsize = (10, 4));
with plt.style.context('grayscale'):
sb.boxplot(x = 'Food Heaviness', data = food_df);
From above plots we can infer that customers are mostly eating light foods and that was probably expected from the nature of process with which was our data gathered. Also food heaviness is mostly between 0.05 and 0.3, while it's range is [0, 2.54].
We will use this feature as our another clustering variable.
clustering_variables.append('Food Heaviness')
Since Calories is the most important food-defining feature for several reasons:
We shall add Calories Category as a new feature.
Also, Foods shall be categorized in 4 categories - low, low-average, high-average and high, denoted by numbers 0 to 3 respectively.
def get_calorie_category(x):
if x <= 60:
return 0
if x <= 176:
return 1
if x <= 300:
return 2
return 3
food_df['Calorie Category'] = food_df['Calories'].apply(get_calorie_category)
food_df.head()
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | ... | Iron | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | Food Heaviness | Calorie Category | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 5.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 9.0 | 0.0 | 0.0 | 0.0 | 21011 | 0.003333 | 0 |
| 1 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 0.0 | 19888 | 0.001333 | 0 |
| 2 | Coffee - Brewed from grounds, 3 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 7.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 14.0 | 0.0 | 0.0 | 0.0 | 7206 | 0.004667 | 0 |
| 3 | Spinach - Raw, 1 cup | 3.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 7.0 | 0.0 | 0.0 | ... | 5.0 | 0.0 | 56.0 | 24.0 | 14.0 | 0.0 | 0.0 | 7251 | 0.004667 | 0 |
| 4 | Eggs - Fried (whole egg), 1 large | 3.0 | 0.0 | 0.0 | 210.0 | 0.0 | 6.0 | 92.0 | 0.0 | 7.0 | ... | 5.0 | 2.0 | 7.0 | 94.0 | 0.0 | 0.0 | 0.0 | 4858 | 0.119667 | 1 |
5 rows × 21 columns
Now let's get to analysing our new feature
plt.figure(figsize = (30, 20))
plt.subplot(235)
sb.countplot(food_df['Calorie Category'], palette = 'Accent')
plt.subplot(236)
food_df['Calorie Category'].value_counts().plot.pie(cmap = 'Accent', autopct = '%1.1f%%');
Now, let's try multivariate analysis with Calorie Category.
plt.figure(figsize = (14, 5))
ax = sb.boxplot(y = 'Calorie Category', x = 'Carbs', data = food_df, orient = 'h');
fig = plt.gcf()
plt.tight_layout()
Correlation between Calories and Carbs is pretty clear from this plot. Let's look at means.
food_df.groupby(['Calorie Category'])['Carbs'].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Calorie Category | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| mean | 3.921183 | 12.329598 | 23.052941 | 43.737891 |
plt.figure(figsize = (14, 5))
ax = sb.boxplot(y = 'Calorie Category', x = 'Fat', data = food_df, orient = 'h');
fig = plt.gcf()
plt.tight_layout()
Correlation between Calories and Fat is pretty clear from this plot. Let's look at means.
food_df.groupby(['Calorie Category'])['Fat'].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Calorie Category | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| mean | 0.917291 | 4.037928 | 8.660002 | 19.794885 |
plt.figure(figsize = (14, 5))
ax = sb.boxplot(y = 'Calorie Category', x = 'Protein', data = food_df, orient = 'h');
fig = plt.gcf()
plt.tight_layout()
Correlation between Calories and Protein is pretty clear from this plot. Let's look at means.
food_df.groupby(['Calorie Category'])['Protein'].agg([np.mean]).sort_values(by='mean', ascending=True).T
| Calorie Category | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| mean | 1.490627 | 6.121392 | 12.141489 | 22.298559 |
These plots are pretty much more detailed visual representation of what correlations table said. We can infer that categories of calories also apply to these 3 nutritions as well. Because, for example high calories food with high probability has high protein too.
We will use this feature as our another clustering variable.
clustering_variables.append('Calorie Category')
Let's create new dataframe that consists of only clustering variables.
clustering_df = food_df[clustering_variables]
clustering_df.head()
| Calories | Carbs | Protein | Fat | Sodium | Sugar | Fiber | Food Heaviness | Calorie Category | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 5.0 | 0.0 | 1.0 | 0.0 | 9.0 | 0.0 | 0.0 | 0.003333 | 0 |
| 1 | 2.0 | 0.0 | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 0.001333 | 0 |
| 2 | 7.0 | 0.0 | 1.0 | 0.0 | 14.0 | 0.0 | 0.0 | 0.004667 | 0 |
| 3 | 7.0 | 1.0 | 1.0 | 0.0 | 24.0 | 0.0 | 1.0 | 0.004667 | 0 |
| 4 | 92.0 | 0.0 | 6.0 | 7.0 | 94.0 | 0.0 | 0.0 | 0.119667 | 1 |
Skewness formula:
def reduce_skewness(col_name, reducer):
clustering_df[col_name] = reducer(clustering_df[col_name])
def draw_probability_density_plots_before_and_after_reducing_skewness(col_name, reducer):
plt.figure(1, figsize = (16, 5))
plt.subplot(121)
plt.title('Old Skewness: ' + str(clustering_df[col_name].skew()))
sb.distplot(clustering_df[col_name], fit = stats.norm);
reduce_skewness(col_name, reducer)
plt.subplot(122)
plt.title('New Skewness: ' + str(clustering_df[col_name].skew()))
sb.distplot(clustering_df[col_name], fit = stats.norm);
draw_probability_density_plots_before_and_after_reducing_skewness('Carbs', np.cbrt)
# cbrt - -0.10
# sqrt - 0.58
# log1p - -0.18
draw_probability_density_plots_before_and_after_reducing_skewness('Fiber', np.cbrt)
# cbrt - 1.48
# sqrt - 1.97
# log1p - 1.88
draw_probability_density_plots_before_and_after_reducing_skewness('Protein', np.cbrt)
# cbrt - 0.08
# sqrt - 0.84
# log1p - 0.22
draw_probability_density_plots_before_and_after_reducing_skewness('Calories', np.cbrt)
# cbrt - 0.0001
# sqrt - 0.64
# log1p - -1.19
draw_probability_density_plots_before_and_after_reducing_skewness('Fat', np.cbrt)
# cbrt - 0.06
# sqrt - 0.76
# log1p - 0.23
draw_probability_density_plots_before_and_after_reducing_skewness('Sodium', np.log1p)
# cbrt - 0.79
# sqrt - 1.45
# log1p - 0.28
draw_probability_density_plots_before_and_after_reducing_skewness('Sugar', np.cbrt)
# cbrt - 1.08
# sqrt - 1.81
# log1p - 1.24
draw_probability_density_plots_before_and_after_reducing_skewness('Food Heaviness', np.cbrt)
# cbrt - 0.04
# sqrt - 0.71
# log1p - 1.59
draw_probability_density_plots_before_and_after_reducing_skewness('Calorie Category', np.log1p)
# cbrt - -0.73
# sqrt - -0.45
# log1p - -0.22
We have only numerical variables in our datasets, but they have different ranges. So, we have to normalize them. Normalization is the second essential preprocessing step of clustering algorithms, since they are based on minimizing the Euclidean distance between the data set points and cluster centroids, which are sensitive to differences in magnitude or scale of the features.
scaler = MinMaxScaler()
clustering_df[clustering_variables] = scaler.fit_transform(clustering_df[clustering_variables])
clustering_df.head()
| Calories | Carbs | Protein | Fat | Sodium | Sugar | Fiber | Food Heaviness | Calorie Category | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.149380 | 0.00000 | 0.219159 | 0.000000 | 0.273011 | 0.0 | 0.00000 | 0.109483 | 0.0 |
| 1 | 0.110064 | 0.00000 | 0.000000 | 0.000000 | 0.212444 | 0.0 | 0.00000 | 0.080668 | 0.0 |
| 2 | 0.167110 | 0.00000 | 0.219159 | 0.000000 | 0.321086 | 0.0 | 0.00000 | 0.122478 | 0.0 |
| 3 | 0.167110 | 0.15874 | 0.219159 | 0.000000 | 0.381653 | 0.0 | 0.29488 | 0.122478 | 0.0 |
| 4 | 0.394365 | 0.00000 | 0.398238 | 0.387828 | 0.539941 | 0.0 | 0.00000 | 0.361171 | 0.5 |
#clustering_df.to_csv('clustering_df.csv')
clustering_df = pd.read_csv('clustering_df.csv')
clustering_df.drop(clustering_df.columns[0], inplace = True, axis = 1)
clustering_df.head()
| Calories | Carbs | Protein | Fat | Sodium | Sugar | Fiber | Food Heaviness | Calorie Category | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.149380 | 0.00000 | 0.219159 | 0.000000 | 0.273011 | 0.0 | 0.00000 | 0.109483 | 0.0 |
| 1 | 0.110064 | 0.00000 | 0.000000 | 0.000000 | 0.212444 | 0.0 | 0.00000 | 0.080668 | 0.0 |
| 2 | 0.167110 | 0.00000 | 0.219159 | 0.000000 | 0.321086 | 0.0 | 0.00000 | 0.122478 | 0.0 |
| 3 | 0.167110 | 0.15874 | 0.219159 | 0.000000 | 0.381653 | 0.0 | 0.29488 | 0.122478 | 0.0 |
| 4 | 0.394365 | 0.00000 | 0.398238 | 0.387828 | 0.539941 | 0.0 | 0.00000 | 0.361171 | 0.5 |
Let's run K-Means 20 times with 1-20 clusters and check total costs. This will help us determine best number of clusters.
costs = []
for k in range(1, 21):
kmeans = KMeans(n_clusters = k)
kmeans.fit(clustering_df)
costs.append(kmeans.inertia_)
plt.figure(figsize = (18, 12))
plt.style.use("fivethirtyeight")
plt.plot(range(1, 21), costs)
plt.xticks(range(1, 21))
plt.xlabel("Number of Clusters")
plt.ylabel("Costs")
plt.show()
previous_cost = 0
for i in range(len(costs)):
print('Cluster Number:', i + 1, ' | Cost:' if i < 9 else '| Cost:', int(costs[i]), '| Cost Change:', int(costs[i] - previous_cost))
previous_cost = costs[i]
Cluster Number: 1 | Cost: 700555 | Cost Change: 700555 Cluster Number: 2 | Cost: 467101 | Cost Change: -233453 Cluster Number: 3 | Cost: 365414 | Cost Change: -101687 Cluster Number: 4 | Cost: 322228 | Cost Change: -43185 Cluster Number: 5 | Cost: 285795 | Cost Change: -36432 Cluster Number: 6 | Cost: 263126 | Cost Change: -22669 Cluster Number: 7 | Cost: 247579 | Cost Change: -15546 Cluster Number: 8 | Cost: 234844 | Cost Change: -12734 Cluster Number: 9 | Cost: 224310 | Cost Change: -10534 Cluster Number: 10 | Cost: 213575 | Cost Change: -10735 Cluster Number: 11 | Cost: 201978 | Cost Change: -11596 Cluster Number: 12 | Cost: 193981 | Cost Change: -7997 Cluster Number: 13 | Cost: 186609 | Cost Change: -7371 Cluster Number: 14 | Cost: 180748 | Cost Change: -5860 Cluster Number: 15 | Cost: 173538 | Cost Change: -7210 Cluster Number: 16 | Cost: 167274 | Cost Change: -6263 Cluster Number: 17 | Cost: 164890 | Cost Change: -2384 Cluster Number: 18 | Cost: 158810 | Cost Change: -6079 Cluster Number: 19 | Cost: 152413 | Cost Change: -6397 Cluster Number: 20 | Cost: 148547 | Cost Change: -3866
kmeans_12 = KMeans(n_clusters = 12)
kmeans_12.fit(clustering_df)
KMeans(n_clusters=12)
#food_df['Cluster'] = kmeans_12.labels_
#food_df.to_csv('clustered_food_df.csv')
clustered_food_df = pd.read_csv('clustered_food_df.csv')
clustered_food_df.drop(clustered_food_df.columns[0], inplace = True, axis = 1)
clustered_food_df.head()
| Name | Calcium | Ply Fat | Carbs | Chol | Fiber | Protein | Calories | Trn Fat | Fat | ... | Sat Fat | Vit A | Sodium | Vit C | Sugar | Mon Fat | Count | Food Heaviness | Calorie Category | Cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Coffee - Brewed from grounds, 2 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 5.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 9.0 | 0.0 | 0.0 | 0.0 | 21011 | 0.003333 | 0 | 6 |
| 1 | Coffee - Brewed from grounds, 1 cup (8 fl oz) | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 5.0 | 0.0 | 0.0 | 0.0 | 19888 | 0.001333 | 0 | 6 |
| 2 | Coffee - Brewed from grounds, 3 cup (8 fl oz) | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 7.0 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 14.0 | 0.0 | 0.0 | 0.0 | 7206 | 0.004667 | 0 | 6 |
| 3 | Spinach - Raw, 1 cup | 3.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 7.0 | 0.0 | 0.0 | ... | 0.0 | 56.0 | 24.0 | 14.0 | 0.0 | 0.0 | 7251 | 0.004667 | 0 | 9 |
| 4 | Eggs - Fried (whole egg), 1 large | 3.0 | 0.0 | 0.0 | 210.0 | 0.0 | 6.0 | 92.0 | 0.0 | 7.0 | ... | 2.0 | 7.0 | 94.0 | 0.0 | 0.0 | 0.0 | 4858 | 0.119667 | 1 | 5 |
5 rows × 22 columns
pca3D = PCA(n_components = 3)
pca3D.fit(clustering_df)
data_3D = pca3D.transform(clustering_df)
pca2D = PCA(n_components = 2)
pca2D.fit(clustering_df)
data_2D = pca2D.transform(clustering_df)
sum(pca3D.explained_variance_ratio_)
0.7873067407004964
sum(pca2D.explained_variance_ratio_)
0.6801654384539727
plt.figure(figsize = (20, 12))
plt.scatter(data_2D[:, 0], data_2D[:, 1], c = kmeans_12.labels_, cmap = 'Paired')
plt.colorbar();
fig = plt.figure(figsize = (20, 16))
ax = fig.add_subplot(111, projection = '3d')
ax.scatter(data_3D[:, 0], data_3D[:, 1], data_3D[:, 2], c = kmeans_12.labels_, cmap = 'Paired')
plt.show()
First of all, let's look at how many elements are in each cluster and their percentage distribution.
plt.figure(figsize = (30, 20))
plt.subplot(234)
sb.countplot(clustered_food_df['Cluster'], palette = 'Paired')
plt.subplot(235)
clustered_food_df['Cluster'].value_counts().plot.pie(cmap = 'Paired', autopct = '%1.1f%%');
From this plot can be seen that count of elements in each cluster varies from 70000 to 200000, which is quite good.
column_name = 'Calcium'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 8 | 6 | 9 | 7 | 2 | 10 | 0 | 5 | 1 | 3 | 4 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.081304 | 0.099496 | 0.148942 | 0.164485 | 0.172524 | 0.194092 | 0.201662 | 0.234888 | 0.381379 | 0.395323 | 0.436357 | 0.605941 |
column_name = 'Ply Fat'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 2 | 6 | 0 | 9 | 7 | 10 | 1 | 3 | 5 | 8 | 11 | 4 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.000025 | 0.000116 | 0.000134 | 0.000156 | 0.000206 | 0.000272 | 0.000478 | 0.000722 | 0.000758 | 0.001016 | 0.001842 | 0.004435 |
column_name = 'Carbs'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 5 | 4 | 9 | 0 | 8 | 10 | 3 | 2 | 1 | 7 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 1.330256 | 1.347696 | 1.867299 | 3.829497 | 5.858071 | 7.223649 | 15.781141 | 18.430581 | 27.967999 | 37.890725 | 42.054391 | 42.407871 |
column_name = 'Chol'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 0 | 2 | 9 | 3 | 10 | 8 | 7 | 1 | 11 | 4 | 5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.032379 | 0.072851 | 0.101245 | 0.133129 | 0.163501 | 0.298641 | 0.498751 | 1.222797 | 1.387591 | 1.452899 | 1.746293 | 2.390589 |
column_name = 'Fiber'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 7 | 6 | 4 | 8 | 5 | 9 | 2 | 10 | 0 | 3 | 11 | 1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.031528 | 0.039885 | 0.049342 | 0.050573 | 0.051002 | 0.28463 | 0.493638 | 0.565519 | 0.750132 | 1.791617 | 2.343404 | 4.487828 |
column_name = 'Protein'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 8 | 6 | 0 | 9 | 2 | 3 | 10 | 1 | 11 | 7 | 4 | 5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.091073 | 0.139475 | 1.771491 | 2.217107 | 2.300138 | 4.240484 | 4.488521 | 15.723908 | 16.787704 | 17.018848 | 21.195836 | 21.280629 |
column_name = 'Calories'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 9 | 0 | 3 | 10 | 2 | 5 | 4 | 8 | 1 | 7 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 13.781178 | 34.76782 | 36.66509 | 120.047138 | 120.501726 | 149.087062 | 185.394411 | 188.746069 | 223.830465 | 347.693014 | 386.555363 | 386.760642 |
column_name = 'Trn Fat'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 0 | 9 | 5 | 6 | 10 | 3 | 8 | 2 | 7 | 4 | 1 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.0 | 0.0 | 0.000035 | 0.000037 | 0.000091 | 0.000109 | 0.000231 | 0.000241 | 0.000312 | 0.000386 | 0.00054 | 0.000554 |
column_name = 'Fat'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 0 | 9 | 2 | 3 | 8 | 10 | 5 | 4 | 1 | 11 | 7 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.472865 | 0.88043 | 1.288858 | 2.428749 | 3.205 | 3.536629 | 4.230245 | 9.362557 | 9.624426 | 13.833622 | 16.175078 | 16.434164 |
column_name = 'Potass.'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 8 | 9 | 10 | 7 | 0 | 2 | 4 | 11 | 1 | 5 | 3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 1.495018 | 2.577327 | 2.697832 | 3.143396 | 3.404427 | 3.78084 | 4.79601 | 5.374921 | 5.583316 | 6.019765 | 6.098277 | 6.788398 |
column_name = 'Iron'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 8 | 9 | 6 | 0 | 2 | 10 | 5 | 7 | 4 | 3 | 1 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.053714 | 0.080239 | 0.088496 | 0.16895 | 0.171281 | 0.216221 | 0.251626 | 0.290039 | 0.414348 | 0.500246 | 0.571634 | 0.906436 |
column_name = 'Sat Fat'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 9 | 0 | 10 | 3 | 8 | 5 | 2 | 7 | 1 | 4 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.007934 | 0.011385 | 0.01193 | 0.04133 | 0.054598 | 0.060112 | 0.077603 | 0.077872 | 0.083567 | 0.13342 | 0.164699 | 0.260595 |
column_name = 'Vit A'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 5 | 7 | 2 | 8 | 1 | 6 | 10 | 9 | 4 | 11 | 0 | 3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.002453 | 0.004276 | 0.006706 | 0.009178 | 0.010957 | 0.013919 | 0.015146 | 0.024449 | 0.028197 | 0.031728 | 0.037167 | 0.051243 |
column_name = 'Sodium'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 4 | 11 | 3 | 0 | 6 | 8 | 2 | 9 | 10 | 5 | 1 | 7 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.117099 | 0.250496 | 0.784348 | 1.006514 | 1.316193 | 1.337764 | 9.98857 | 147.370213 | 222.818514 | 346.051675 | 650.508396 | 703.183544 |
column_name = 'Vit C'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 7 | 4 | 5 | 10 | 9 | 6 | 8 | 2 | 11 | 0 | 1 | 3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.018599 | 0.022326 | 0.022653 | 0.023594 | 0.029744 | 0.035613 | 0.040776 | 0.04597 | 0.053256 | 0.057026 | 0.073861 | 0.083676 |
column_name = 'Sugar'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 5 | 8 | 4 | 3 | 9 | 0 | 1 | 10 | 11 | 7 | 2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.170442 | 0.181593 | 0.19765 | 0.306775 | 0.760533 | 1.222358 | 2.229235 | 2.766029 | 3.491159 | 4.205525 | 10.339923 | 20.781009 |
column_name = 'Mon Fat'
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 2 | 6 | 9 | 0 | 10 | 1 | 7 | 5 | 8 | 3 | 4 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.0 | 0.00007 | 0.000071 | 0.000094 | 0.000186 | 0.000191 | 0.000348 | 0.000401 | 0.000403 | 0.001193 | 0.001628 | 0.002 |
column_name = 'Food Heaviness'
plt.figure(figsize = (12, 6))
ax = sb.boxplot(x = 'Cluster', y = column_name, data = clustered_food_df);
fig = plt.gcf()
plt.tight_layout()
clustered_food_df.groupby(['Cluster'])[column_name].agg([np.mean]).sort_values(by = 'mean', ascending = True).T
| Cluster | 6 | 9 | 0 | 3 | 10 | 8 | 5 | 4 | 2 | 1 | 11 | 7 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mean | 0.014264 | 0.042068 | 0.046642 | 0.11181 | 0.138861 | 0.18001 | 0.202828 | 0.208079 | 0.258171 | 0.365516 | 0.42067 | 0.463588 |
column_name = 'Calorie Category'
plt.figure(figsize = (15, 8))
sb.countplot(x = 'Cluster', hue = 'Calorie Category', data = clustered_food_df);
| Carbs | Fiber | Protein | Calories | Fat | Sodium | Sugar | Food Heaviness | Calorie Category | |
|---|---|---|---|---|---|---|---|---|---|
| Cluster 0 | Low; Mean: 5.8; N8 | Average; Mean: 0.75; N4 | Low; Mean: 1.77; N10 | Low; Mean: 36; N10 | Low; Mean: 0.88; N11 | Low; Mean: 1; N9 | Average; Mean: 2.22; N6 | Low; Mean: 0.04; N10 | Low |
| Cluster 1 | High; Mean: 37.8; N3 | High; Mean: 4.48; N1 | Average; Mean: 15.7; N5 | High; Mean: 347; N3 | High; Mean: 13.8; N3 | High; Mean: 650; N2 | Average; Mean: 2.76; N5 | High; Mean: 0.36; N3 | High |
| Cluster 2 | Average; Mean: 28; N4 | Average; Mean: 0.49; N6 | Low; Mean: 2.3; N8 | Average; Mean: 149; N7 | Low; Mean: 2.42; N9 | Low; Mean: 9.98; N6 | High; Mean: 20.7; N1 | Average; Mean: 0.25; N4 | Average |
| Cluster 3 | Average; Mean: 18.4; N5 | High; Mean: 1.79; N3 | Low; Mean: 4.24; N7 | Average; Mean: 120; N9 | Low; Mean: 3.2; N8 | Low; Mean: 0.78; N10 | Low; Mean: 0.76; N8 | Average; Mean: 0.11; N9 | Average |
| Cluster 4 | Low; Mean: 1.8; N10 | Low; Mean: 0.04; N10 | High; Mean: 21.2; N2 | Average; Mean: 188; N5 | Average; Mean: 9.62; N4 | Low; Mean: 0.11; N12 | Low; Mean: 0.3; N9 | Average; Mean: 0.2; N5 | Average |
| Cluster 5 | Low; Mean: 1.3; N11 | Low; Mean: 0.05; N8 | High; Mean: 21.3; N1 | Average; Mean: 185; N6 | Average; Mean: 9.36; N5 | Average; Mean: 346; N3 | Low; Mean: 0.18; N11 | Average; Mean: 0.2; N6 | Average |
| Cluster 6 | Low; Mean: 1.3; N12 | Low; Mean: 0.03; N11 | Low; Mean: 0.14; N11 | Low; Mean: 13; N12 | Low; Mean: 0.47; N12 | Low; Mean: 1.31; N8 | Low; Mean: 0.17; N12 | Low; Mean: 0.01; N12 | Low |
| Cluster 7 | High; Mean: 42; N2 | Low; Mean: 0.03; N12 | Average; Mean: 17; N3 | High; Mean: 386; N2 | High; Mean: 16.4; N1 | High; Mean: 703; N1 | High; Mean: 10.3; N2 | High; Mean: 0.46; N1 | High |
| Cluster 8 | Low; Mean: 7.2; N7 | Low; Mean: 0.05; N9 | Low; Mean: 0.09; N12 | Average; Mean: 223; N4 | Low; Mean: 3.53; N7 | Low; Mean: 1.33; N7 | Low; Mean: 0.19; N10 | Average; Mean: 0.18; N7 | Average |
| Cluster 9 | Low; Mean: 3.8; N9 | Average; Mean: 0.28; N7 | Low; Mean: 2.21; N9 | Low; Mean: 34; N11 | Low; Mean: 1.28; N10 | Average; Mean: 147; N5 | Average; Mean: 1.22; N7 | Low; Mean: 0.04; N11 | Low |
| Cluster 10 | Average; Mean: 15.7; N6 | Average; Mean: 0.55; N5 | Low; Mean: 4.48; N6 | Average; Mean: 120; N8 | Low; Mean: 4.23; N6 | Average; Mean: 222; N4 | Average; Mean: 3.49; N4 | Average; Mean: 0.13; N8 | Average |
| Cluster 11 | High; Mean: 42; N1 | High; Mean: 2.34; N2 | Average; Mean: 16.8; N4 | High; Mean: 386; N1 | High; Mean: 16.1; N2 | Low; Mean: 0.25; N11 | Average; Mean: 4.2; N3 | High; Mean: 0.42; N2 | High |
Let's start with most notable clusters.
Most notable one is probably Cluster 7, It has low fiber, average protein and everythin other high. This cluster probably would be for unhealthy and heavy food. First example that comes to minds is Snickers, after checking its consistency it is clear that Snickers is the average representative of this cluster. Also pizzas and shawarmas would belong here too.
Next cluster is Cluster 11, it is also pretty heavy but it has low sodium and not high sugar while having high fiber and carbs. Low sodium means that there is not much Smoked, cured, salted or canned meat, also no pizzas, buritos and shawarmas, this in combination with high fiber and carbs means that it has more food made of dough like Khachapuri and so on.
Cluster 1 is only notably different from Cluster 11 with having high sodium average, while having little bit less of heaviness. This would be ideal cluster for sandwiches and stuff like that.
Now that we are out of high calories clusters first thing that strikes the eye is Cluster 2, by having abnormally high sugar average, this cluster would definitely be for sugar sweets, other stats further encourage this statement too. But heavy sweets like snickers would still go in Cluster 7 though.
If we look at Cluster 5 it would be ideal cluster for Mtsvadi or eggs with low carbs, fiber, high protein and higher average of sodium and calories. Also average heaviness.
Cluster 3 would be ideal cluster for pastas/rice and stuff like that, because of high fiber, high end of cabrs. and average calories as well as heaviness.
Cluster 6 is definitely for some drinks and lightest of foods, like water, cofee or strict diet foods/vegetables.
Cluster 10 is more like for fruits, banana would be ideal representative of this cluster.
Cluster 0 is definitely for porridges, cereals, berries and such light diet food.
Cluster 4 would be for chicken breast, cheese, yogurt and other healthy foods with high protein and average calorie values.
As for Clusters 8 and Cluster 9 they would still be for different kinds of fruits and diet foods.
To reinforce our problem, we can use another clustering method - SOMPY (Self-organizing map), which is a type of artificial neural network that is trained using unsupervised learning to produce two-dimensional, discretized representation of the input space of the training samples, called a map.
import glob
import random
import joblib
from sompy.sompy import SOMFactory
from sompy.visualization.mapview import View2D
from sompy.visualization.hitmap import HitMapView
from sompy.visualization.bmuhits import BmuHitsView
from sompy.visualization.plot_tools import plot_hex_map
At first, let's define some useful variables:
data = clustering_df.values
names = clustering_variables
And now let's train the model with different parameters. The more, the better. Each iteration is stored in disk for further study. For demonstration purposes we created only 20 models, since it takes too much time for building each of them. So, this code will be commented.
# for i in range(20):
# sm = SOMFactory().build(data,
# mapsize = [random.choice(list(range(15, 25))), random.choice(list(range(10, 15)))],
# initialization = 'random',
# component_names = names,
# lattice = 'hexa')
# sm.train(n_job = 4, verbose = False, train_rough_len = 30, train_finetune_len = 100)
# joblib.dump(sm, 'model_{}.joblib'.format(i))
Study the models trained and plot the errors obtained in order to select the best one.
models_pool = glob.glob('./models/model*')
errors = []
for model_filepath in models_pool:
sm = joblib.load(model_filepath)
topographic_error = sm.calculate_topographic_error()
quantization_error = sm.calculate_quantization_error()
errors.append((topographic_error, quantization_error))
e_top, e_q = zip(*errors)
find_bmu took: 5.985000 seconds find_bmu took: 7.082000 seconds find_bmu took: 7.266000 seconds find_bmu took: 4.922000 seconds find_bmu took: 6.204000 seconds find_bmu took: 5.073000 seconds find_bmu took: 8.297000 seconds find_bmu took: 8.253000 seconds find_bmu took: 7.473000 seconds find_bmu took: 5.863000 seconds find_bmu took: 6.862000 seconds find_bmu took: 5.826000 seconds find_bmu took: 6.205000 seconds find_bmu took: 7.111000 seconds find_bmu took: 6.224000 seconds find_bmu took: 6.845000 seconds find_bmu took: 8.930000 seconds find_bmu took: 8.121000 seconds find_bmu took: 6.701000 seconds find_bmu took: 8.621000 seconds find_bmu took: 7.529000 seconds find_bmu took: 5.622000 seconds find_bmu took: 6.220000 seconds find_bmu took: 5.710000 seconds find_bmu took: 11.520000 seconds find_bmu took: 17.442000 seconds find_bmu took: 15.645000 seconds find_bmu took: 17.453000 seconds find_bmu took: 20.902000 seconds find_bmu took: 20.483000 seconds find_bmu took: 13.477000 seconds find_bmu took: 16.230000 seconds find_bmu took: 14.157000 seconds find_bmu took: 13.115000 seconds find_bmu took: 15.725000 seconds find_bmu took: 14.372000 seconds find_bmu took: 11.238000 seconds find_bmu took: 6.474000 seconds find_bmu took: 5.948000 seconds find_bmu took: 4.998000 seconds find_bmu took: 5.707000 seconds find_bmu took: 5.064000 seconds find_bmu took: 4.670000 seconds find_bmu took: 5.672000 seconds find_bmu took: 5.223000 seconds find_bmu took: 8.045000 seconds find_bmu took: 11.917000 seconds find_bmu took: 11.053000 seconds find_bmu took: 7.514000 seconds find_bmu took: 8.226000 seconds find_bmu took: 7.016000 seconds find_bmu took: 9.512000 seconds find_bmu took: 11.014000 seconds find_bmu took: 9.012000 seconds find_bmu took: 12.098000 seconds find_bmu took: 15.098000 seconds find_bmu took: 15.899000 seconds find_bmu took: 10.158000 seconds find_bmu took: 9.334000 seconds find_bmu took: 8.025000 seconds
plt.scatter(e_top, e_q)
plt.xlabel('Topographic Error')
plt.ylabel('Quantization Error')
plt.show()
We need to minimize topographic error, so we will manually choose model with the lowest topographic error.
selected_model = 0
sm = joblib.load(models_pool[selected_model])
topographic_error = sm.calculate_topographic_error()
quantization_error = sm.calculate_quantization_error()
print ('Topographic error = %s\nQuantization error = %s' % (topographic_error, quantization_error))
find_bmu took: 10.252000 seconds find_bmu took: 11.523000 seconds find_bmu took: 12.095000 seconds
Topographic error = 0.05377802793051527 Quantization error = 0.34064460338018965
The components map shows the values of the variables for each prototype and allows us to extract conclusions consisting of non-linear patterns between variables. It shows the patterns learned by the neural network which are used to determine de winning neuron of each training instance.
view2D = View2D(20, 20, '', text_size = 8)
view2D.show(sm, col_sz = 5, which_dim = 'all', denormalize = True)
plt.show()
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6A6B3D4A8>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6A6B3D4A8> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6A4E19EF0>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6A4E19EF0> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6AFF48630>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6AFF48630> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6B79B99E8>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6B79B99E8> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6A4E35080>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6A4E35080> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6B1FA0780>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6B1FA0780> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A580F72F98>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A580F72F98> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6AEF6A7F0>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6AEF6A7F0> on colorbar
Setting pcolormesh
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6B4FFE4E0>
Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6B4FFE4E0> on colorbar
Setting pcolormesh
findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=8.0.
findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 3.05
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 2.335
findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335
findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 3.335
findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 2.05
findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (courbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Candara' (Candaral.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Calibri' (calibril.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Candara' (Candarali.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified' (HPSimplified_BdIt.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal 900 normal>) = 11.525
findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal 700 condensed>) = 1.535
findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'BPG Courier S GPL&GNU' (BPG_Courier_S_GPL&GNU.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 0.25
findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_BdIt.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HoloLens MDL2 Assets' (holomdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Arial' (arialbd.ttf) normal normal 700 normal>) = 0.33499999999999996
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Corbel' (corbell.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ariblk.ttf) normal normal 900 normal>) = 0.525
findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 1.25
findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candarali.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal 900 normal>) = 11.525
findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constanb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 1.25
findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candarab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified' (HPSimplified_It.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Times New Roman' (times.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Courier New' (courbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Outlook' (OUTLOOK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Arial' (arialbi.ttf) italic normal 700 normal>) = 1.335
findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 1.05
findfont: score(<Font 'Constantia' (constanb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified' (HPSimplified.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal 700 condensed>) = 1.535
findfont: score(<Font 'Courier New' (courbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbelli.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal 700 condensed>) = 0.5349999999999999
findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal 800 normal>) = 10.43
findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candaral.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 0.25
findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 0.05
findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal 800 normal>) = 10.43
findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candarab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Arial' (arialbd.ttf) normal normal 700 normal>) = 0.33499999999999996
findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal 700 condensed>) = 11.535
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal 600 normal>) = 11.24
findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Constantia' (constanz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_Rg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal 900 normal>) = 11.525
findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Constantia' (constanz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Arial' (arialbi.ttf) italic normal 700 normal>) = 1.335
findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Courier New' (courbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal 900 normal>) = 11.525
findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'HP Simplified' (HPSimplified_Rg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibril.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Ink Free' (Inkfree.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal 350 normal>) = 11.0975
findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal 700 condensed>) = 0.5349999999999999
findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 0.05
findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified' (HPSimplified_LtIt.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified' (HPSimplified.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'HP Simplified' (HPSimplified_It.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Marlett' (marlett.ttf) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'HP Simplified' (HPSimplified_LtIt.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Arial' (ariblk.ttf) normal normal 900 normal>) = 0.525
findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal 350 normal>) = 11.0975
findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'MT Extra' (MTEXTRA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbell.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_Lt.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 1.05
findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HoloLens MDL2 Assets' (holomdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Corbel' (corbelli.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal 600 normal>) = 11.24
findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal 500 normal>) = 10.145
findfont: score(<Font 'MS Outlook' (OUTLOOK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal 700 condensed>) = 11.535
findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Times New Roman' (times.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Ink Free' (Inkfree.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_Lt.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal 700 normal>) = 10.335
findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=8.0 to Arial ('C:\\Windows\\Fonts\\arial.ttf') with score of 0.050000.
findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=11.52.
findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05
findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 3.05
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 2.335
findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335
findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 3.335
findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 2.05
findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (courbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Candara' (Candaral.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Calibri' (calibril.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Candara' (Candarali.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified' (HPSimplified_BdIt.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal 900 normal>) = 11.525
findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal 700 condensed>) = 1.535
findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'BPG Courier S GPL&GNU' (BPG_Courier_S_GPL&GNU.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITEDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Comic Sans MS' (comic.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans' (LSANSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHVIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_R.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Condensed' (ROCC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Bodoni MT' (BOD_CI.TTF) italic normal 400 condensed>) = 11.25
findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Engravers MT' (ENGR.TTF) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Kristen ITC' (ITCKRIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe MDL2 Assets' (segmdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Symbol' (symbol.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 0.25
findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Mistral' (MISTRAL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARAIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bell MT' (BELL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Broadway' (BROADW.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Viner Hand ITC' (VINERITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Freestyle Script' (FREESCPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Microsoft New Tai Lue' (ntailub.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_BdIt.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Yu Gothic' (YuGothR.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Print' (segoeprb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HoloLens MDL2 Assets' (holomdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tempus Sans ITC' (TEMPSITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Comic Sans MS' (comicz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Arial' (arialbd.ttf) normal normal 700 normal>) = 0.33499999999999996
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Book' (FRABKIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (seguisbi.ttf) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Corbel' (corbell.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Forte' (FORTE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Copperplate Gothic Bold' (COPRGTB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Centaur' (CENTAUR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CR.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gadugi' (gadugi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comicbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI Symbol' (seguisym.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ariblk.ttf) normal normal 900 normal>) = 0.525
findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Calisto MT' (CALISTB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'MS Reference Sans Serif' (REFSAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 1.25
findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constan.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (couri.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAXDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Nirmala UI' (Nirmala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgunbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambria.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAXD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Juice ITC' (JUICE___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Onyx' (ONYX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft YaHei' (msyhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Verdana' (verdanai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Calisto MT' (CALISTBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Century Gothic' (GOTHICBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Bookman Old Style' (BOOKOS.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Corbel' (corbeli.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe UI' (segoeuii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed Extra Bold' (TCCEB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candarali.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Californian FB' (CALIFB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (seguibli.ttf) italic normal 900 normal>) = 11.525
findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Nirmala UI' (NirmalaB.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI Emoji' (seguiemj.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constanb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Bodoni MT' (BOD_BLAR.TTF) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Goudy Stout' (GOUDYSTO.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell' (ROCKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Times New Roman' (timesi.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Arial' (ARIALNI.TTF) italic normal 400 condensed>) = 1.25
findfont: score(<Font 'Vladimir Script' (VLADIMIR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Kunstler Script' (KUNSTLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wingdings 2' (WINGDNG2.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Elephant' (ELEPHNT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candarab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Times New Roman' (timesbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Rockwell' (ROCKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Impact' (impact.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified' (HPSimplified_It.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Bodoni MT' (BOD_PSTC.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Times New Roman' (times.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Courier New' (courbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Goudy Old Style' (GOUDOSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Sans Unicode' (l_10646.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Niagara Engraved' (NIAGENG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Outlook' (OUTLOOK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Arial' (arialbi.ttf) italic normal 700 normal>) = 1.335
findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Blackadder ITC' (ITCBLKAD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Gill Sans Ultra Bold' (GILSANUB.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Demi Cond' (FRADMCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft PhagsPa' (phagspa.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 1.05
findfont: score(<Font 'Constantia' (constanb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gigi' (GIGI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Trebuchet MS' (trebucbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Garamond' (GARA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Himalaya' (himalaya.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Schoolbook' (CENSCBK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Haettenschweiler' (HATTEN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe UI' (segoeuil.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Book Antiqua' (ANTQUABI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Calibri' (calibrii.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified' (HPSimplified.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Arial' (ARIALNBI.TTF) italic normal 700 condensed>) = 1.535
findfont: score(<Font 'Courier New' (courbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbelli.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal 700 condensed>) = 0.5349999999999999
findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gabriola' (Gabriola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Cooper Black' (COOPBL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Jokerman' (JOKERMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Console' (lucon.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal 800 normal>) = 10.43
findfont: score(<Font 'Cambria' (cambriai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Harrington' (HARNGTON.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Magneto' (MAGNETOB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'MingLiU-ExtB' (mingliub.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium' (framd.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Old Style' (GOUDOSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Book Antiqua' (ANTQUAB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Old English Text MT' (OLDENGL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Mongolian Baiti' (monbaiti.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Corbel' (corbelz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candaral.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Constantia' (constani.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Algerian' (ALGER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALN.TTF) normal normal 400 condensed>) = 0.25
findfont: score(<Font 'Vivaldi' (VIVALDII.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Playbill' (PLAYBILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Comic Sans MS' (comici.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'MV Boli' (mvboli.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Curlz MT' (CURLZ___.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgunsl.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 0.05
findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Consolas' (consola.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Extra Bold' (ROCKEB.TTF) normal normal 800 normal>) = 10.43
findfont: score(<Font 'Lucida Fax' (LFAXI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Papyrus' (PAPYRUS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelawUI.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candarab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bodoni MT' (BOD_CB.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Arial' (arialbd.ttf) normal normal 700 normal>) = 0.33499999999999996
findfont: score(<Font 'Baskerville Old Face' (BASKVILL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Harlow Solid Italic' (HARLOWSI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Calibri' (calibrib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Georgia' (georgia.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Britannic Bold' (BRITANIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal 700 condensed>) = 11.535
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal 600 normal>) = 11.24
findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Webdings' (webdings.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Berlin Sans FB' (BRLNSB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bell MT' (BELLI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft YaHei' (msyh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookshelf Symbol 7' (BSSYM7.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Heavy' (FRAHV.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Stencil' (STENCIL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Calligraphy' (LCALLIG.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Constantia' (constanz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Century Schoolbook' (SCHLBKBI.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wingdings 3' (WINGDNG3.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Javanese Text' (javatext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (palabi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Tahoma' (tahomabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_Rg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Yu Gothic' (YuGothB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Californian FB' (CALIFI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft Yi Baiti' (msyi.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Felix Titling' (FELIXTI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal 900 normal>) = 11.525
findfont: score(<Font 'Tw Cen MT' (TCMI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Gill Sans Ultra Bold Condensed' (GILLUBCD.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (seguili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Franklin Gothic Book' (FRABK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Garamond' (GARABD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Agency FB' (AGENCYB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Brush Script MT' (BRUSHSCI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Chiller' (CHILLER.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Georgia' (georgiaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Lucida Sans' (LSANS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua Titling MT' (PERTILI.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Constantia' (constanz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Ebrima' (ebrima.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft YaHei' (msyhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bookman Old Style' (BOOKOSI.TTF) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Ravie' (RAVIE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'High Tower Text' (HTOWERT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Gothic' (msgothic.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Demi' (FRADMIT.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Modern No. 20' (MOD20.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Maiandra GD' (MAIAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Verdana' (verdana.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified Jpan' (hpsimplifiedjpan-regular.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Sans Serif' (micross.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PERI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Arial' (arialbi.ttf) italic normal 700 normal>) = 1.335
findfont: score(<Font 'Bernard MT Condensed' (BERNHC.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Bodoni MT' (BOD_I.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Century Gothic' (GOTHICB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Courier New' (courbi.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Snap ITC' (SNAP____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Cambria' (cambriaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Bodoni MT' (BOD_BLAI.TTF) italic normal 900 normal>) = 11.525
findfont: score(<Font 'Gloucester MT Extra Condensed' (GLECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Tw Cen MT' (TCBI____.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'Century Schoolbook' (SCHLBKI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Candara' (Candara.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wide Latin' (LATINWD.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'Tw Cen MT Condensed' (TCCB____.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'HP Simplified' (HPSimplified_Rg.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOSBI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Tw Cen MT' (TCM_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibril.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Ink Free' (Inkfree.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calisto MT' (CALISTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Sitka Small' (SitkaI.ttc) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe UI' (segoeuib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Yu Gothic' (YuGothL.ttc) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Segoe UI' (segoeui.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Poor Richard' (POORICH.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_B.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Century Schoolbook' (SCHLBKB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal 350 normal>) = 11.0975
findfont: score(<Font 'Edwardian Script ITC' (ITCEDSCR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Sitka Small' (Sitka.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (ARIALNB.TTF) normal normal 700 condensed>) = 0.5349999999999999
findfont: score(<Font 'Tahoma' (tahoma.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'French Script MT' (FRSCRIPT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial' (arial.ttf) normal normal 400 normal>) = 0.05
findfont: score(<Font 'Eras Demi ITC' (ERASDEMI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Script' (segoescb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft New Tai Lue' (ntailu.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Bell MT' (BELLB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Palatino Linotype' (palai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Sitka Small' (SitkaZ.ttc) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Trebuchet MS' (trebucit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Corbel' (corbel.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Bahnschrift' (bahnschrift.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified Hans' (hpsimplifiedhans-light.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Verdana' (verdanab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Calibri' (calibrili.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Calibri' (calibri.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Handwriting' (LHANDW.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Franklin Gothic Medium' (framdit.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Showcard Gothic' (SHOWG.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Verdana' (verdanaz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'SimSun' (simsun.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century Gothic' (GOTHICI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HP Simplified' (HPSimplified_LtIt.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Berlin Sans FB' (BRLNSR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Medium ITC' (ERASMD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Arial Rounded MT Bold' (ARLRDBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT Condensed' (GILC____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Franklin Gothic Demi' (FRADM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans' (LSANSD.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Cambria' (cambriab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Lucida Sans' (LSANSDI.TTF) italic normal 600 normal>) = 11.24
findfont: score(<Font 'Segoe UI' (segoeuisl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gill Sans MT' (GILI____.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Segoe Print' (segoepr.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Myanmar Text' (mmrtext.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'HP Simplified' (HPSimplified.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Gill Sans MT' (GIL_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Times New Roman' (timesbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Footlight MT Light' (FTLTLT.TTF) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'HP Simplified' (HPSimplified_It.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Marlett' (marlett.ttf) normal normal 500 normal>) = 10.145
findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Franklin Gothic Medium Cond' (FRAMDCN.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'HP Simplified' (HPSimplified_LtIt.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Nirmala UI' (NirmalaS.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Segoe UI' (seguisb.ttf) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Californian FB' (CALIFR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbelb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Candara' (Candaraz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEO.TTF) oblique normal 400 normal>) = 11.05
findfont: score(<Font 'Perpetua' (PERBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Arial' (ariblk.ttf) normal normal 900 normal>) = 0.525
findfont: score(<Font 'Segoe UI' (seguisli.ttf) italic normal 350 normal>) = 11.0975
findfont: score(<Font 'Segoe UI Historic' (seguihis.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'OCR A Extended' (OCRAEXT.TTF) normal normal 400 expanded>) = 10.25
findfont: score(<Font 'MT Extra' (MTEXTRA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Corbel' (corbell.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Imprint MT Shadow' (IMPRISHA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Informal Roman' (INFROMAN.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe UI' (seguibl.ttf) normal normal 900 normal>) = 10.525
findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Bright' (LBRITED.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Microsoft Tai Le' (taileb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Trebuchet MS' (trebucbd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_Lt.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Arial' (ariali.ttf) italic normal 400 normal>) = 1.05
findfont: score(<Font 'Book Antiqua' (ANTQUAI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'HoloLens MDL2 Assets' (holomdl2.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Monotype Corsiva' (MTCORSVA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bookman Old Style' (BOOKOSB.TTF) normal normal 600 normal>) = 10.24
findfont: score(<Font 'Corbel' (corbelli.ttf) italic normal 300 normal>) = 11.145
findfont: score(<Font 'Lucida Sans Typewriter' (LTYPEBO.TTF) oblique normal 600 normal>) = 11.24
findfont: score(<Font 'Rage Italic' (RAGE.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Microsoft PhagsPa' (phagspab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Pristina' (PRISTINA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Courier New' (cour.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'High Tower Text' (HTOWERTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Bradley Hand ITC' (BRADHITC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Malgun Gothic' (malgun.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Rockwell Condensed' (ROCCB___.TTF) normal normal 700 condensed>) = 10.535
findfont: score(<Font 'Myanmar Text' (mmrtextb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Yu Gothic' (YuGothM.ttc) normal normal 500 normal>) = 10.145
findfont: score(<Font 'MS Outlook' (OUTLOOK.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Fax' (LFAX.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua Titling MT' (PERTIBD.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Colonna MT' (COLONNA.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Matura MT Script Capitals' (MATURASC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Georgia' (georgiab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Eras Bold ITC' (ERASBD.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Lucida Bright' (LBRITEI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Berlin Sans FB Demi' (BRLNSDB.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Castellar' (CASTELAR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Wingdings' (wingding.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Elephant' (ELEPHNTI.TTF) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Lucida Bright' (LBRITE.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT' (TCB_____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Book Antiqua' (BKANT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Perpetua' (PER_____.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Parchment' (PARCHM.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Century' (CENTURY.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palatino Linotype' (pala.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bodoni MT' (BOD_CBI.TTF) italic normal 700 condensed>) = 11.535
findfont: score(<Font 'SimSun-ExtB' (simsunb.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhl.ttc) normal normal 290 normal>) = 10.1545
findfont: score(<Font 'Leelawadee UI' (LeelaUIb.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Century Gothic' (GOTHIC.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Times New Roman' (times.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Ink Free' (Inkfree.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Leelawadee UI' (LeelUIsl.ttf) normal normal 350 normal>) = 10.0975
findfont: score(<Font 'Trebuchet MS' (trebuc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft Tai Le' (taile.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calisto MT' (CALIST.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Segoe Script' (segoesc.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'MS Reference Specialty' (REFSPCL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gill Sans MT' (GILBI___.TTF) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Gill Sans MT Ext Condensed Bold' (GLSNECB.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Perpetua' (PERB____.TTF) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Goudy Old Style' (GOUDOS.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Palace Script MT' (PALSCRI.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Eras Light ITC' (ERASLGHT.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Bauhaus 93' (BAUHS93.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Consolas' (consolab.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Copperplate Gothic Light' (COPRGTL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Sitka Small' (SitkaB.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Microsoft JhengHei' (msjh.ttc) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Microsoft JhengHei' (msjhbd.ttc) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Ebrima' (ebrimabd.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'Agency FB' (AGENCYR.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Gadugi' (gadugib.ttf) normal normal 700 normal>) = 10.335
findfont: score(<Font 'HP Simplified' (HPSimplified_Lt.ttf) normal normal 300 normal>) = 10.145
findfont: score(<Font 'Sylfaen' (sylfaen.ttf) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Calibri' (calibriz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Niagara Solid' (NIAGSOL.TTF) normal normal 400 normal>) = 10.05
findfont: score(<Font 'Tw Cen MT Condensed' (TCCM____.TTF) normal normal 400 condensed>) = 10.25
findfont: score(<Font 'Segoe UI' (segoeuiz.ttf) italic normal 700 normal>) = 11.335
findfont: score(<Font 'Candara' (Candarai.ttf) italic normal 400 normal>) = 11.05
findfont: score(<Font 'Script MT Bold' (SCRIPTBL.TTF) normal normal 700 normal>) = 10.335
findfont: Matching sans\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=11.52 to Arial ('C:\\Windows\\Fonts\\arial.ttf') with score of 0.050000.
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This visualization is very important because it shows how the instances are spreaded across the hexagonal lattice. The more instances lay into a cell, the more instances it is representing and hence the more we have to take it into acount.
vhts = BmuHitsView(12, 12, 'Hits Map', text_size = 8)
vhts.show(sm, anotate = True, onlyzeros = False, labelsize = 8, cmap = 'autumn', logaritmic = False)
plt.show()
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A580F76C88> Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A580F76C88> on colorbar Setting pcolormesh top of axes not in the figure, so title not moved top of axes not in the figure, so title not moved top of axes not in the figure, so title not moved top of axes not in the figure, so title not moved
Now, let's try to divide our data into 12 clusters. This visualization helps us to focus on the groups which share similar characteristics.
sm.cluster(12)
hits = HitMapView(12, 12, 'Clustering', text_size = 10, cmap = plt.cm.jet)
a = hits.show(sm, anotate = True, onlyzeros = False, labelsize = 8, cmap = 'Paired')
plt.show()
locator: <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6BFB517F0> Using auto colorbar locator <matplotlib.colorbar._ColorbarAutoLocator object at 0x000001A6BFB517F0> on colorbar Setting pcolormesh
We can make use of K-Means' clusters to simplify process of explaining SOMPY's clusters. For example, looking at the above described components map, we can notice that Calories are gathered mostly in the upper right corner. Using similar logic for every nutrition, we can conclude that, for example, SOMPY's 10th, 7th and 2nd clusters are similar to K_means' 11th, 7th and 1st clusters respectively. In addition, 6th clusters are same in both cluster sets, since they have lowest values for each nutrition. Observing mainly Protein and also other components we can infer that 3rd cluster in SOMPY is similar with 5th cluster in K-Means. 4th (SOMPY) and 9th (K-Means) clusters are also similar. Same goes for 11th (SOMPY) and 2nd (K-Means), 5th (SOMPY) and 3rd K-means and so on.
After clustering food we can start talking about usage of these clusters. As we mentioned in the start of this project there are many ways of using food clusters. These clusters can be useful both for app development and marketing reasons. We can briefly overview several of our usage ideas:
First usage of our clusters could be for improving service with personalized suggestions. For example, if someone is taking more Calories than their goal, we could suggest food from the category that is closest to his intake, but has lower Calories consistency.
As we know in the modern society a lot of peopel have trouble starting diet from a scratch. So, to address that issue, we can build a diet plan that starts from the current Food Heaviness level of user and reduces it gradually, suggesting food from different categories.
Other usage could be for personalized advertisements. For example, if we know that user has tendency of eating food from 11th category (K-Means), we can show them McDonald's ad (probably we aren't supposed to that, since it is a healthy lifestyle app, but it's ok just for an example). Also, if user takes food daily from any category we can advertise other food from that category.